{"topic":"ML & AI Methods","items":[{"title":"Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors","url":"/papers/arxiv/2609.27051/","summary":"Proposes a statistical referee that judges investment factors proposed by language-model agents using out-of-sample market outcomes, ensuring false-discovery control at any stopping time.","featured":"2026-09-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":4,"scale":"fanfare"},{"title":"AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining","url":"/papers/arxiv/2609.29014/","summary":"Proposes a multi-agent system with post-training that automates alpha factor mining locally, using diverse research paths and joint optimization to broaden exploration while maintaining prediction quality.","featured":"2026-09-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"fanfare"},{"title":"Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents","url":"/papers/arxiv/2609.28876/","summary":"Introduces a replayable environment combining 1,568 resolved prediction-market questions with 18.8M dated news articles to benchmark and train language-model forecasting agents on historical data.","featured":"2026-09-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"fanfare"},{"title":"Artificial intelligence and financial markets","url":"/papers/ssrn/7515878/","summary":"A survey examines how AI transforms information production, intermediation, and market structure, with implications for efficiency, competition and financial stability.","featured":"2026-09-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"fanfare"},{"title":"Label alchemy: Target engineering for improved stock selection","url":"/papers/ssrn/7494298/","summary":"Reshaping the prediction target through location, scale and shape transformations raises long-short Sharpe from 0.68 to 1.69, with label choice mattering more than model choice.","featured":"2026-09-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"fanfare"},{"title":"Memorisation or Alpha? Detecting Look-Ahead Contamination in Cross-Sectional Equity Signals","url":"/papers/ssrn/7490302/","summary":"Testing whether a large language model ranks stocks by forecasting or memory, the study finds a significant information-coefficient gap of 0.185 inside versus outside its training window, suggesting substantial look-ahead contamination.","featured":"2026-09-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"fanfare"},{"title":"Beta Recall, Alpha Recall, and a Contamination Detector that Needs No Labels * Measuring Training-data Leakage in LLM Equity Signals","url":"/papers/ssrn/7485818/","summary":"The study measures recall versus forecasting in an LLM's stock rankings by comparing cross-sectional information coefficients inside and outside the training window.","featured":"2026-09-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"fanfare"},{"title":"Execution-Aware Alpha Mining: Teaching LLM Factor Agents to Account for Trading Costs","url":"/papers/ssrn/7498983/","summary":"The paper builds a closed-loop system where an LLM proposes equity factors penalized for execution costs and shows that accounting for trading costs dramatically improves net performance.","featured":"2026-09-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"fanfare"},{"title":"Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing","url":"/papers/repec/nbr-nberwo-35431/","summary":"Optimized AI systems analyzing earnings call transcripts double explained variation in stock returns versus standard benchmarks while improving interpretability through human-readable decision rules.","featured":"2026-09-25","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":4,"scale":"fanfare"},{"title":"Predicting Financial Market Stress with Machine Learning","url":"/papers/repec/cpr-ceprdp-20439/","summary":"Tree-based machine learning models predict the full distribution of financial market stress 27% better than traditional time-series methods, with macro uncertainty and monetary policy expectations as key drivers.","featured":"2026-09-25","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":3,"scale":"fanfare"},{"title":"Ex Machina: Financial Stability in the Age of Artificial Intelligence","url":"/papers/repec/cpr-ceprdp-20681/","summary":"Q-learning and large language model investors generate systematically different behaviors in fund redemption settings, with Q-learning showing excessive coordination and amplified fragility under default risk.","featured":"2026-09-25","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":3,"scale":"fanfare"},{"title":"Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?","url":"/papers/arxiv/2603.29121/","summary":"The paper discusses a model that suggests combining human effort with partial automation is usually cheaper and more effective than fully automating complex tasks.","featured":"2026-04-03","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":0,"scale":"shares"},{"title":"The Inference Bottleneck: Antitrust and Neutrality Duties in the Age of Cognitive Infrastructure","url":"/papers/arxiv/2602.22750/","summary":"The article explains that the rise of generative AI is changing competition by focusing on ongoing decision-making, which can lead to unfair practices in the market. It suggests a solution called Neutral Inference to promote fairness and transparency.","featured":"2026-03-04","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":0,"scale":"shares"},{"title":"Bounded Normative Equivalence in Human-AI Cooperation: Group Behaviour, Not Partner Labels, Predicts Cooperation under Anonymous Aggregate Feedback","url":"/papers/arxiv/2601.20487/","summary":"AI agents in group settings can influence cooperation just like humans do, showing that social norms can adjust for both AI and human participants.","featured":"2026-02-02","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":0,"scale":"shares"},{"title":"Gingado: A Machine Learning Library Focused on Economics and Finance","url":"/papers/ssrn/4482553/","summary":"ML for Economics: Gingado is a developing Python library that helps incorporate machine learning into economic research by enhancing datasets and evaluating models.","featured":"2025-12-28","label":"SSRN","topic":"ML & AI Methods","cites":5,"score":163,"scale":"shares"},{"title":"Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks","url":"/papers/ssrn/4478013/","summary":"Analyzing Japanese stock charts with CNN reveals predictive patterns for returns, independent of common momentum trends.","featured":"2025-12-28","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":181,"scale":"shares"},{"title":"The Banker in Your Social Network","url":"/papers/ssrn/4466139/","summary":"The research indicates that social financial advice significantly boosts stock market participation, especially through close social ties.","featured":"2025-12-28","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":314,"scale":"shares"},{"title":"SigMA: Path Signatures and Multi-head Attention for Learning Parameters in fBm-driven SDEs","url":"/papers/arxiv/2512.15088/","summary":"The SigMA neural architecture improves parameter estimation in stochastic differential equations using path signatures and self-attention, outperforming traditional methods across multiple datasets.","featured":"2025-12-19","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":1,"scale":"shares"},{"title":"Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise","url":"/papers/arxiv/2512.14967/","summary":"A new numerical method combines elicitability and deep learning for McKean-Vlasov stochastic equations, enabling efficient training of neural networks without expensive simulations, tested successfully on financial models.","featured":"2025-12-19","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":1,"scale":"shares"},{"title":"Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes","url":"/papers/arxiv/2512.14991/","summary":"An adaptive reinforcement learning algorithm enhances learning in controlled diffusion processes by partitioning state-action spaces, providing theoretical guarantees and effective results in applications like portfolio selection.","featured":"2025-12-19","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":1,"scale":"shares"},{"title":"Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies","url":"/papers/arxiv/2512.10913/","summary":"Reinforcement learning improves financial decision-making by simplifying complex investment problems, emphasizing clear explanations and strong reliability over complex algorithms.","featured":"2025-12-14","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"When Medical AI Explanations Help and When They Harm","url":"/papers/arxiv/2512.08424/","summary":"AIgenerated explanations can improve decision-making when algorithms are right, but can mislead when they're wrong, highlighting a paradox in AI transparency for doctors.","featured":"2025-12-14","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":0,"scale":"shares"},{"title":"Measuring Computer Science Enthusiasm: A Questionnaire-Based Analysis of Age and Gender Effects on Students' Interest","url":"/papers/arxiv/2512.08472/","summary":"Research indicates that age affects students' interest in computer science more than gender, suggesting educational approaches should consider developmental changes to boost engagement.","featured":"2025-12-14","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":0,"scale":"shares"},{"title":"The Adoption and Usage of AI Agents: Early Evidence from Perplexity","url":"/papers/arxiv/2512.07828/","summary":"A study of AI agent use with the Comet browser reveals that personal productivity and learning are the main reasons users interact with these tools.","featured":"2025-12-14","label":"arXiv","topic":"ML & AI Methods","cites":14,"score":0,"scale":"shares"},{"title":"AI-Powered Direct Indexing: Exploring Thematic Universes for Enhanced Risk-Adjusted Returns","url":"/papers/ssrn/4977007/","summary":"The research presents FINDALL, a search engine that effectively identifies relevant stocks for direct indexing, outperforming traditional ETFs with lower costs.","featured":"2025-12-01","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":264,"scale":"shares"},{"title":"The Risk-Adjusted Intelligence Dividend: A Quantitative Framework for Measuring AI Return on Investment Integrating ISO 42001 and Regulatory Exposure","url":"/papers/arxiv/2511.21975/","summary":"Organizations using AI face challenges in traditional ROI calculations due to risks, leading to a new framework that includes risk assessments.","featured":"2025-12-01","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Standardized Threat Taxonomy for AI Security, Governance, and Regulatory Compliance","url":"/papers/arxiv/2511.21901/","summary":"The use of AI in regulated industries exposes gaps between technical risk assessments and compliance, prompting a new taxonomy for AI risk evaluation and financial impacts.","featured":"2025-12-01","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":0,"scale":"shares"},{"title":"Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions","url":"/papers/arxiv/2511.21802/","summary":"Research shows that large language models can indirectly collaborate in Dutch auctions to increase prices, influenced by market structure.","featured":"2025-12-01","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":0,"scale":"shares"},{"title":"The Economics of AI Training Data: A Research Agenda","url":"/papers/arxiv/2510.24990/","summary":"Lays out data economics: why data is special, documents AI training-data deals, proposes a hierarchy of data units, and lists key research questions.","featured":"2025-11-04","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":22,"scale":"shares"},{"title":"Estimating Nationwide High-Dosage Tutoring Expenditures: A Predictive Model Approach","url":"/papers/arxiv/2510.24899/","summary":"Machine learning on incomplete ESSER plans estimates U.S. school districts spent about $2.2 billion on high‑dosage tutoring during COVID.","featured":"2025-11-04","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Open Materials Generation with Stochastic Interpolants","url":"/papers/arxiv/2502.02582/","summary":"Generative Model for Crystal Discovery: OMatG: a generative framework using stochastic interpolants and symmetry-aware (equivariant) crystal representations to design stable inorganic crystals, setting a new state of the art.","featured":"2025-11-04","label":"Machine learning","topic":"ML & AI Methods","cites":30,"score":3,"scale":"shares"},{"title":"Reinforcement Learning and Consumption-Savings Behavior","url":"/papers/arxiv/2510.20748/","summary":"Qlearning with neural nets shows low‑asset unemployed spend stimulus more and that past unemployment leads to persistently lower consumption.","featured":"2025-10-27","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Parental environment and student achievement: Does a Matthew effect exist?","url":"/papers/arxiv/2510.18481/","summary":"Using Madrid data, parents' education and effort boost kids' grades, with the parental advantage shrinking in math, peaking then falling in literature, and steadily rising in English.","featured":"2025-10-27","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Government Transparency and Innovation: Evidence from Wireless Products","url":"/papers/arxiv/2510.19377/","summary":"Publishing a detailed product database nearly doubled follow-up innovation—mainly by foreign firms—though the boost weakened over time.","featured":"2025-10-27","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Cost Estimation with ML","url":"/papers/repec/pkp-rocere-2019-p-64-75/","summary":"The article introduces a machine learning method for predicting software costs early in a project with high accuracy.","featured":"2025-10-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":42,"scale":"shares"},{"title":"Predictive economics: Rethinking economic methodology with machine learning","url":"/papers/arxiv/2510.04726/","summary":"The article introduces predictive economics, a unique analytical approach in economics that combines machine learning, interpretability, and theoretical structure for better out-of-sample performance.","featured":"2025-10-09","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Neural Network Convergence for Variational Inequalities","url":"/papers/arxiv/2509.26535/","summary":"A novel approach to using neural networks on linear parabolic variational inequalities shows the potential of neural networks in solving optimal stopping and control problems in finance.","featured":"2025-10-03","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":9,"scale":"shares"},{"title":"The AI Productivity Index (APEX)","url":"/papers/arxiv/2509.25721/","summary":"The AI Productivity Index (APEX) is a new benchmark for evaluating the economic value of AI models in sectors like investment banking, consulting, law, and medical care.","featured":"2025-10-03","label":"arXiv","topic":"ML & AI Methods","cites":10,"score":64,"scale":"shares"},{"title":"Cognitive and non-cognitive efficiency gaps between private and public schools and their determinants in the Latin America region–a hybrid data envelopment analysis and interpretable machine learning approach based on PISA 2022","url":"/papers/arxiv/2509.25353/","summary":"A study reveals a performance gap favoring private schools in Latin America, with home resources and school autonomy being key efficiency determinants.","featured":"2025-10-03","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":15,"scale":"shares"},{"title":"Identifying the post-pandemic determinants of low performing students in Latin America through interpretable Machine Learning methods","url":"/papers/arxiv/2509.24508/","summary":"A paper uses PISA 2022 data and machine learning to identify factors affecting student performance in Latin America, emphasizing the need for strategies to address educational inequalities.","featured":"2025-10-03","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":15,"scale":"shares"},{"title":"Academic resilience in the Latin America region post COVID-19 pandemic -- an explainable machine learning analysis of its determinants and heterogeneity using alternative definitions","url":"/papers/arxiv/2509.24830/","summary":"The research highlights household resources, gender, homework, and teaching quality as key factors influencing academic resilience among disadvantaged Latin American students, especially during the pandemic.","featured":"2025-10-03","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":14,"scale":"shares"},{"title":"Enhancing OHLC Data with Timing Features: A Machine Learning Evaluation","url":"/papers/arxiv/2509.16137/","summary":"The research indicates that adding timing data to machine learning models can enhance the prediction of volume-weighted average price (VWAP).","featured":"2025-09-22","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":9,"scale":"shares"},{"title":"Deep Learning in the Sequence Space","url":"/papers/arxiv/2509.13623/","summary":"The paper presents a deep learning algorithm designed to estimate functional rational expectations equilibria in dynamic stochastic economies, demonstrating its effectiveness through three progressively complex economies.","featured":"2025-09-22","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":7,"scale":"shares"},{"title":"AI and jobs. A review of theory, estimates, and evidence","url":"/papers/arxiv/2509.15265/","summary":"The article examines the impact of Generative AI on employment and the macroeconomy, noting significant but context-dependent productivity gains, and calls for more research into adoption dynamics and effects on expertise.","featured":"2025-09-22","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":6,"scale":"shares"},{"title":"Leveraging Artificial Intelligence as a Strategic Growth Catalyst for Small and Medium-sized Enterprises","url":"/papers/arxiv/2509.14532/","summary":"The report emphasizes the significant benefits of AI for SMEs, with 91% reporting increased revenue, and potential for reducing operational costs by 30% and saving over 20 hours monthly.","featured":"2025-09-22","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"From Digital Distrust to Codified Honesty: Experimental Evidence on Generative AI in Credence Goods Markets","url":"/papers/arxiv/2509.06069/","summary":"Large language models (LLMs) in expert services have pros and cons, with human markets being more efficient, but LLMs potentially reducing trust and overshadowing experts' preferences, while also improving experts' communication of their goals.","featured":"2025-09-13","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":8,"scale":"shares"},{"title":"Nested Optimal Transport Distances","url":"/papers/arxiv/2509.06702/","summary":"The article introduces a new, faster, and more efficient algorithm for generative AI in financial decision-making applications, improving the computation of the nested optimal transport distance.","featured":"2025-09-13","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":12,"scale":"shares"},{"title":"Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation","url":"/papers/arxiv/2509.08163/","summary":"The paper suggests a new framework for promoting fairness in machine learning, especially in regression tasks and situations with multiple protected attributes, by reducing the link between model predictions and protected attributes.","featured":"2025-09-13","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":9,"scale":"shares"},{"title":"Working with AI: Measuring the Applicability of Generative AI to Occupations","url":"/papers/arxiv/2507.07935/","summary":"AI is primarily used in work activities for information gathering and writing, particularly in knowledge-based roles like computer and administrative support.","featured":"2025-09-13","label":"arXiv","topic":"ML & AI Methods","cites":27,"score":3971,"scale":"shares"},{"title":"Combined machine learning for stock selection strategy based on dynamic weighting methods","url":"/papers/arxiv/2508.18592/","summary":"The research proposes a stock selection strategy using combined machine learning algorithms, with Information Coefficients-based weighting showing superior results in returns and predictive performance.","featured":"2025-08-29","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":7,"scale":"shares"},{"title":"General Social Agents","url":"/papers/arxiv/2508.17407/","summary":"Modern AI agents can be used to apply social science theories to new settings with little or no modification, and have been found to predict human behavior more accurately than traditional methods in a sample of new games.","featured":"2025-08-29","label":"arXiv","topic":"ML & AI Methods","cites":13,"score":6,"scale":"shares"},{"title":"Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs","url":"/papers/arxiv/2508.14995/","summary":"The research demonstrates how generative equilibrium operators, a type of Neural Operator, can solve complex optimization problems with a realistic number of parameters, bridging the gap between theory and practice.","featured":"2025-08-29","label":"arXiv","topic":"ML & AI Methods","cites":7,"score":16,"scale":"shares"},{"title":"Periodic evaluation of defined-contribution pension fund: A dynamic risk measure approach","url":"/papers/arxiv/2508.05241/","summary":"A novel framework is introduced for evaluating pension funds periodically, using a dynamic risk measure and a learning algorithm to manage risk and find optimal investment strategies.","featured":"2025-08-12","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":9,"scale":"shares"},{"title":"Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework","url":"/papers/arxiv/2508.05491/","summary":"The article proposes a comprehensive Systematic AI-driven Startup Evaluation (SAISE) Framework to improve the fragmented approach to integrating AI into startup evaluation.","featured":"2025-08-12","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization","url":"/papers/arxiv/2502.03435/","summary":"The study explores memorization in denoising score matching, revealing a regularization mechanism driven by large learning rates that prevents excessive closeness to the empirical optimal score, thus reducing memorization.","featured":"2025-08-12","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":14,"scale":"shares"},{"title":"Generative AI in Higher Education: Evidence from an Elite College","url":"/papers/arxiv/2508.00717/","summary":"Evidence from Elite College: More than 80% of students at a selective U.S. college have started using AI for academic purposes within two years of the release of ChatGPT. The usage varies across different subjects and student demographics, and institutional policies may affect usage patterns and have varying impacts.","featured":"2025-08-07","label":"arXiv","topic":"ML & AI Methods","cites":19,"score":1,"scale":"shares"},{"title":"AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society","url":"/papers/arxiv/2507.19911/","summary":"The study uses Tokyo to analyze the impact of AI on urban distribution, suggesting AI could counteract 60-80% of productivity loss due to aging.","featured":"2025-08-07","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":18,"scale":"shares"},{"title":"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity","url":"/papers/arxiv/2507.17099/","summary":"AI technology combining weather prediction and route optimization can potentially double taxi driver earnings, indicating a promising market in weather intelligence.","featured":"2025-07-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":10,"scale":"shares"},{"title":"Left Leaning Models: How AI Evaluates Economic Policy?","url":"/papers/arxiv/2507.15771/","summary":"Large language models used in economics are found to be more sensitive to issues like unemployment, inequality, financial stability, and environmental harm, and less responsive to traditional macroeconomic factors.","featured":"2025-07-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":17,"scale":"shares"},{"title":"Explainable Graph Neural Networks via Structural Externalities","url":"/papers/arxiv/2507.17848/","summary":"GraphEXT, a new explainability framework for Graph Neural Networks, improves their explainability by focusing on node interactions and the effect of structural changes on predictions.","featured":"2025-07-25","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":12,"scale":"shares"},{"title":"Alternative Loss Function in Evaluation of Transformer Models","url":"/papers/arxiv/2507.16548/","summary":"The study uses Mean Absolute Directional Loss function to evaluate machine learning models in quantitative finance, revealing that Transformer models are superior to LSTM models.","featured":"2025-07-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":12,"scale":"shares"},{"title":"Advancing AI Capabilities and Evolving Labor Outcomes","url":"/papers/arxiv/2507.08244/","summary":"The research indicates that increased AI usage leads to higher unemployment and shorter work hours, especially among older, younger, male, and college-educated workers.","featured":"2025-07-17","label":"arXiv","topic":"ML & AI Methods","cites":7,"score":9,"scale":"shares"},{"title":"Artificial Finance: How AI Thinks About Money","url":"/papers/arxiv/2507.10933/","summary":"The study finds that large language models (LLMs) show a risk-neutral approach to financial decision-making, sometimes produce inconsistent responses, and their overall responses are similar to those of participants from Tanzania.","featured":"2025-07-17","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":7,"scale":"shares"},{"title":"What Matters Most? A Quantitative Meta-Analysis of AI-Based Predictors for Startup Success","url":"/papers/arxiv/2507.09675/","summary":"AI startup success is influenced by firm characteristics, investor structure, digital/social traction, and funding history, but these factors are context-dependent and influenced by data accessibility.","featured":"2025-07-17","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":8,"scale":"shares"},{"title":"NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance","url":"/papers/arxiv/2507.09601/","summary":"A Meta-Analysis: The NMIXX model, fine-tuned with high-confidence triplets, excels in capturing financial semantics in low-resource languages like Korean, emphasizing the importance of tokenizer design in cross-lingual settings.","featured":"2025-07-17","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":7,"scale":"shares"},{"title":"Strategic Alignment Patterns in National AI Policies","url":"/papers/arxiv/2507.05400/","summary":"A new visual mapping method has been introduced for evaluating strategic alignment in national artificial intelligence policies, identifying unique alignment archetypes across governance models and offering practical advice for policymakers.","featured":"2025-07-10","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":6,"scale":"shares"},{"title":"Epistemic Scarcity: The Economics of Unresolvable Unknowns","url":"/papers/arxiv/2507.01483/","summary":"The article argues that AI systems are incapable of performing key functions of economic coordination or creating norms, interpreting institutions, or taking responsibility, challenging the belief in AI's ability to maintain economic and epistemic order.","featured":"2025-07-10","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":21,"scale":"shares"},{"title":"Autonomy by Design: Preserving Human Autonomy in AI Decision-Support","url":"/papers/arxiv/2506.23952/","summary":"The article explores the effects of AI on specialized fields, noting its potential to diminish skill and value. It suggests a framework for creating AI systems that maintain human autonomy in these areas.","featured":"2025-07-03","label":"arXiv","topic":"ML & AI Methods","cites":30,"score":6,"scale":"shares"},{"title":"RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI","url":"/papers/arxiv/2506.19958/","summary":"RobustiPy is a new Python tool for analyzing model uncertainty, providing efficient methods for confidence intervals, model selection, and out-of-sample evaluation.","featured":"2025-07-03","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":32,"scale":"shares"},{"title":"Cloud-Native AI Framework","url":"/papers/ssrn/5237913/","summary":"The article emphasizes the necessity for Big Tech firms to revamp their cloud infrastructures for better handling of machine learning tasks, and offers a guiding framework for this transformation.","featured":"2025-06-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Simulation Study for Port Flows","url":"/papers/ssrn/5239631/","summary":"The research uses machine learning and a simulation model to improve accuracy in analyzing import container flows at the Port of New York-New Jersey.","featured":"2025-06-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":27,"scale":"shares"},{"title":"AI in Tourism: Kerala Study","url":"/papers/ssrn/5242805/","summary":"Kerala Study: The study introduces the SMART AI-Driven Tourism Marketing Framework to boost tourist engagement in Kerala, using AI chatbots, predictive analytics, and personalized content.","featured":"2025-06-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Racial Challenges of AI in Economics","url":"/papers/ssrn/5237156/","summary":"The article claims that creating fair and racially-just machine learning is currently unachievable due to reasons like biased training data and opaque algorithm design.","featured":"2025-06-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Artificial Intelligence and Relationship Lending","url":"/papers/ssrn/5241807/","summary":"The research explores the impact of AI adoption in credit scoring and relationship lending by banks, suggesting that AI investments can help banks manage the effects of relationship lending on credit supply and decisions.","featured":"2025-06-25","label":"SSRN","topic":"ML & AI Methods","cites":3,"score":33,"scale":"shares"},{"title":"WPI Students' Financial Knowledge","url":"/papers/ssrn/5245648/","summary":"Despite a strong interest in learning about financial preparedness, college students lack financial literacy and understanding of investing and debt.","featured":"2025-06-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":43,"scale":"shares"},{"title":"Social Group Bias in AI Finance","url":"/papers/arxiv/2506.17490/","summary":"The study reveals racial bias in large language models used in financial decisions, and proposes a control-vector intervention that can decrease these biases by up to 70% without impacting the model's performance.","featured":"2025-06-25","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":8,"scale":"shares"},{"title":"Artificial Intelligence, Lean Startup Method, and Product Innovations","url":"/papers/arxiv/2506.16334/","summary":"The Lean Startup Method enhances AI's impact on product innovation in startups, reducing uncertainties, facilitating product development, and enabling high-quality production in less time.","featured":"2025-06-25","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":7,"scale":"shares"},{"title":"Social Group Bias in AI Finance","url":"/papers/ssrn/5287153/","summary":"The article examines racial bias in financial decision-making models, suggesting a method to reduce racial disparities without affecting model performance.","featured":"2025-06-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"On Quantum Bsde Solver for High-Dimensional Parabolic Pdes","url":"/papers/arxiv/2506.14612/","summary":"The research introduces a quantum machine learning method for approximating solutions to complex partial differential equations, showing that Variational Quantum Circuits offer better accuracy and lower variance, especially in highly nonlinear situations.","featured":"2025-06-18","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":6,"scale":"shares"},{"title":"EconGym: A Scalable AI Testbed with Diverse Economic Tasks","url":"/papers/arxiv/2506.12110/","summary":"The article presents EconGym, a scalable testbed that integrates various economic tasks with AI algorithms for large-scale simulations and policy optimization in economic research.","featured":"2025-06-18","label":"arXiv","topic":"ML & AI Methods","cites":10,"score":8,"scale":"shares"},{"title":"Towards an Approach for Evaluating the Impact of AI Standards The use case of entity resolution","url":"/papers/arxiv/2506.13839/","summary":"The concept paper introduces an analytical method to assess the influence of AI standards on innovation and trust, using existing evaluation frameworks and encouraging dialogue on its potential among stakeholders.","featured":"2025-06-18","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":7,"scale":"shares"},{"title":"Algorithmic Bias Anti-Discrimination Law","url":"/papers/ssrn/5283387/","summary":"The article explores the legal consequences of predictive uncertainty in machine learning systems under UK anti-discrimination law, stressing the significance of policy and design decisions.","featured":"2025-06-11","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"AI Casino Regulation in Macao","url":"/papers/ssrn/5285013/","summary":"The article announces the advent of the AI Casino era.","featured":"2025-06-11","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Fraud Detection Diffusion Model","url":"/papers/ssrn/5285870/","summary":"The paper presents a class-balanced diffusion model to enhance credit card fraud detection, using a two-stage process to improve the quality of minority-class samples and remove noisy synthetic samples.","featured":"2025-06-11","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Artificial Intelligence in Tax Administration: Enhancing Compliance, Transparency, and Ethical Governance","url":"/papers/ssrn/5285760/","summary":"The article discusses the potential of AI, particularly NLP, ML, and intelligent chatbots, to improve tax administration, while also considering the ethical and regulatory challenges of AI deployment.","featured":"2025-06-11","label":"SSRN","topic":"ML & AI Methods","cites":5,"score":2,"scale":"shares"},{"title":"How reinforcement learning can drive personalized financial wellness","url":"/papers/ssrn/5276883/","summary":"The study suggests a new approach combining reinforcement learning, behavioral analytics, and natural language processing for personalized financial advice.","featured":"2025-06-11","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Customer Behavior Prediction in E-Commerce","url":"/papers/ssrn/5284346/","summary":"The article explores the application of data analytics and machine learning in ecommerce for predicting customer behavior and optimizing marketing strategies.","featured":"2025-06-11","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Delphos: A reinforcement learning framework for assisting discrete choice model specification","url":"/papers/arxiv/2506.06410/","summary":"A new framework based on deep reinforcement learning has been introduced to enhance the process of discrete choice modelling, adapting strategies dynamically without needing prior domain knowledge.","featured":"2025-06-11","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":6,"scale":"shares"},{"title":"From Axioms to Algorithms: Mechanized Proofs of the vNM Utility Theorem","url":"/papers/arxiv/2506.07066/","summary":"The von Neumann-Morgenstern expected utility theorem has been thoroughly formalized using the Lean 4 interactive theorem prover, offering a solid base for applications in economic modeling, AI alignment, and management decision systems.","featured":"2025-06-11","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Can Artificial Intelligence Trade the Stock Market?","url":"/papers/arxiv/2506.04658/","summary":"Deep Reinforcement Learning algorithms, specifically DDQN and PPO, are effective in stock market trading, providing better risk-adjusted returns than traditional methods.","featured":"2025-06-11","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":17,"scale":"shares"},{"title":"Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives","url":"/papers/arxiv/2410.19202/","summary":"A new AI framework has been created to quickly produce prebunking strategies against misinformation, which has been shown to decrease belief in election rumors and boost faith in election integrity across political divides.","featured":"2025-06-11","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":237,"scale":"shares"},{"title":"High-Dimensional Learning in Finance","url":"/papers/arxiv/2506.03780/","summary":"A study provides theoretical and empirical evidence for understanding the circumstances and methods through which machine learning achieves predictive success in finance, suggesting that successful predictions are more likely to come from simpler factors rather than complex mechanisms.","featured":"2025-06-11","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":20,"scale":"shares"},{"title":"My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions","url":"/papers/arxiv/2506.03707/","summary":"A study with a European bank reveals customers are more likely to follow investment advice from a human-AI collaboration than pure AI, indicating human involvement can improve consumer outcomes.","featured":"2025-06-11","label":"arXiv","topic":"ML & AI Methods","cites":9,"score":14,"scale":"shares"},{"title":"Machine Learning Rare Earth Alloys","url":"/papers/ssrn/5279446/","summary":"The authors have created advanced machine learning models to expedite the research and development process of advanced RE Al alloys.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Imbalanced Node Classification Exploration","url":"/papers/ssrn/5279529/","summary":"The authors introduce a new method, Topological Node Exploration and Suppression, to tackle the problem of imbalanced class distribution in graph data for semi-supervised learning in Graph Machine Learning.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Fracture Characterization Hybrid HMA","url":"/papers/ssrn/5276534/","summary":"The study uses AI and machine learning to detect and measure crack length development in semi-circular bending beam specimens, showcasing the effectiveness of the YOLOv8 algorithm in predicting crack lengths.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"High-D Learning in Finance","url":"/papers/ssrn/5281959/","summary":"The article investigates the role of machine learning in financial forecasting, focusing on the impact of standardization in Random Fourier Features and the challenges of learning in low signal-to-noise environments.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"The Role Of Machine Learning In Predicting Market Crashes And Preventing Flash Crashes 2024","url":"/papers/ssrn/5278265/","summary":"The research discusses the role of Machine Learning in predicting market crashes and flash crashes, and the complexities involved.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Generative AI for Synthetic Data Creation","url":"/papers/ssrn/5268010/","summary":"The paper discusses the use of Generative AI models for synthetic data generation, and how synthetic data can enhance model performance and facilitate privacy-preserving data sharing.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Crime Prediction with Data Mining","url":"/papers/ssrn/5266531/","summary":"The paper employs machine learning and deep learning models to predict and categorize crime, with the RNN-LSTM model proving most accurate.","featured":"2025-06-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance","url":"/papers/arxiv/2506.01423/","summary":"The article discusses an AI-based framework for ERP systems that combines AI and business process modeling to automate complex tasks, reducing processing time and errors.","featured":"2025-06-04","label":"arXiv","topic":"ML & AI Methods","cites":13,"score":26,"scale":"shares"},{"title":"AI Financial Advisory","url":"/papers/ssrn/5268858/","summary":"AI-powered roboadvisors are transforming wealth management by improving accessibility and efficiency, despite issues such as data privacy and regulatory obstacles.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Optimal Learning Schedules","url":"/papers/ssrn/5272363/","summary":"A link between stochastic approximation and Kalman filtering has been found, leading to an online algorithm that adaptively tracks variances and achieves optimal learning rates.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Neural Network Logarithm Entropy Estimator","url":"/papers/ssrn/5274641/","summary":"A new LogDet estimator has been proposed to address the challenges of handling high-dimensional samples in machine learning using entropy estimators.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Global Implications of AI in Investment Advisory","url":"/papers/ssrn/5270350/","summary":"The article discusses the transformative effects of AI on the global financial system, including benefits and potential issues like algorithmic bias and data privacy.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Elastic Structures Machine Learning","url":"/papers/ssrn/5263512/","summary":"A new approach for linear elasticity problems combines machine learning and the Matrix Discrete Empirical Interpolation Method to efficiently estimate problem output sensitivities.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Hybrid Machine Learning for Malicious Mobile Apps","url":"/papers/ssrn/5267367/","summary":"The research presents a new method for detecting Android malware, using machine learning and permission analysis.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning for Corporate Fraud Detection","url":"/papers/ssrn/5263459/","summary":"The study assesses the use of machine learning in detecting accounting fraud, aiming to compare its effectiveness with traditional models.","featured":"2025-05-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Comparative analysis of financial data differentiation techniques using LSTM neural network","url":"/papers/arxiv/2505.19243/","summary":"The study finds that using fractional differentiation in data preparation enhances the forecasting performance of predictive models in financial time series, compared to traditional logarithmic returns.","featured":"2025-05-30","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":21,"scale":"shares"},{"title":"When Do AI Gains Become Broadly Shareable? A Policy Threshold for AI-Driven Automation","url":"/papers/arxiv/2505.18687/","summary":"The study suggests that AI systems need to be 5-6 times more productive than current automation to finance a universal basic income without additional taxes or job creation.","featured":"2025-05-30","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":44,"scale":"shares"},{"title":"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures","url":"/papers/arxiv/2505.23025/","summary":"The research uses large language models to create a dataset of capital control measures across 196 countries, contributing to the use of these models in economics.","featured":"2025-05-30","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":17,"scale":"shares"},{"title":"Distributionally Robust Deep Q-Learning","url":"/papers/arxiv/2505.19058/","summary":"The paper presents a new robust Q-learning algorithm for continuous state spaces, optimizing for the worst-case scenario, with applications like portfolio optimization.","featured":"2025-05-30","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":22,"scale":"shares"},{"title":"A Mathematical Framework for AI-Human Integration in Work","url":"/papers/arxiv/2505.23432/","summary":"The paper presents a mathematical framework modeling the role of Generative AI in job scenarios, showing it enhances human skills rather than replacing them, especially benefiting lower-skilled workers.","featured":"2025-05-30","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":25,"scale":"shares"},{"title":"The AI Penalty: People Reduce Compensation for Workers Who Use AI","url":"/papers/arxiv/2501.13228/","summary":"A study finds that people tend to lower compensation for workers using AI tools, a trend called AI Penalization, indicating that AI adoption in the workplace could increase worker inequality.","featured":"2025-05-30","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":20,"scale":"shares"},{"title":"RLDAUNCE: Reinforcement Learning for Data","url":"/papers/ssrn/5259995/","summary":"Reinforcement Learning for Data: The article presents RLDAUNCE, a method that improves data assimilation using physical constraints, focusing on uncertainty quantification and computational efficiency.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning IV Estimators","url":"/papers/ssrn/5258814/","summary":"The paper highlights the challenges of nonparametric instrumental variable estimation, proposing machine learning instrumental variable algorithms for better performance through advanced regularization techniques.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"RealTime Earthquake Intensity ML","url":"/papers/ssrn/5262668/","summary":"The research suggests a machine learning model for quick earthquake damage assessment using operational data from base station service providers, showing high accuracy and real-time functionality.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Predicting Work Accidents with ML","url":"/papers/ssrn/5259984/","summary":"The study assesses the effectiveness of dimensionality reduction methods in predicting occupational accidents in retail, finding that Forward Feature Selection combined with the Gradient Boosting Classifier is most effective.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Undetected Accounting Fraud: Implications for Theory and Machine Learning Predictive Models","url":"/papers/ssrn/5259405/","summary":"The study uses machine learning to identify non-fraud instances in financial fraud research, improving inference and addressing undetected frauds.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Optimizing UHTC Oxidation Resistance","url":"/papers/ssrn/5261508/","summary":"The study introduces an intelligent optimization framework using generative adversarial networks and active learning to tackle issues in the high-temperature oxidation resistance of ultrahigh temperature ceramics.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI Agent for SME Loan Origination","url":"/papers/ssrn/5259658/","summary":"The study presents a hybrid multiagent architecture that uses structured financial metrics and unstructured borrower intent to address the loan acquisition challenges faced by SMEs.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Hybrid Framework for Dam Breach Prediction","url":"/papers/ssrn/5261596/","summary":"The research introduces a hybrid framework that combines the BREACH model's physical mechanisms with machine learning to accurately predict dam breach parameters for disaster risk reduction.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Portable Alpha Implementation","url":"/papers/ssrn/5257786/","summary":"The article explores the use of a convolutional neural network-technical analysis model and unsupervised learning in implementing the portable alpha strategy, allowing investors to isolate returns from market index exposure.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"LowRank Matrix Completion","url":"/papers/ssrn/5259117/","summary":"A new Gradient Descent-based solution for low-rank matrix completion in data science and machine learning provides efficient recovery and robust convergence guarantees.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Predicting Supply Chain Disruptions","url":"/papers/ssrn/5257242/","summary":"Machine learning can predict disruptions and optimize recovery strategies in supply chains, improving resilience and reducing costs.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Feature Engineering in ML","url":"/papers/ssrn/5248179/","summary":"Real-time machine learning strategies based on fundamental signals provide significant results, highlighting the importance of feature engineering in investment strategies.","featured":"2025-05-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Deciphering the AI Economy: A Mathematical Model Perspective","url":"/papers/arxiv/2505.11991/","summary":"The research shows a positive link between artificial intelligence growth and GDP per Capita, suggesting a 23.9% AI increase is needed for a 1% GDP per Capita rise.","featured":"2025-05-21","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":16,"scale":"shares"},{"title":"The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today","url":"/papers/arxiv/2505.09620/","summary":"The article discusses a machine learning model that accurately predicts house prices using macro-economic factors, outperforming existing indices.","featured":"2025-05-21","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":18,"scale":"shares"},{"title":"Curricular Renewal: A Faculty and Student Guide to Maximizing Educational Investment","url":"/papers/ssrn/5248333/","summary":"The paper highlights the gap between academic training and practical AI skills, suggesting improvements for higher education models.","featured":"2025-05-14","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"Multimodal AI for Disability Inclusion: Breaking Barriers in Assistive Technologies","url":"/papers/ssrn/5251186/","summary":"The paper discusses the use of multimodal AI in assistive devices for disability inclusion, and the ethical considerations involved.","featured":"2025-05-14","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"IronBased Fenton Catalysts Prep","url":"/papers/ssrn/5249395/","summary":"A study suggests using iron-based catalysts prepared by pyrolysis for resource utilization, with machine learning predicting catalyst performance and analyzing key factors.","featured":"2025-05-14","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Economic Crises Prediction with ML","url":"/papers/ssrn/5237256/","summary":"The research uses machine learning to predict national financial crises, with the Balanced Random Forest model proving most effective.","featured":"2025-05-14","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"ML in Wealth Management","url":"/papers/ssrn/5249044/","summary":"The study discusses the transformation of the wealth management industry through machine learning and cloud computing, improving competitiveness and efficiency.","featured":"2025-05-14","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Can Generative AI agents behave like humans? Evidence from laboratory market experiments","url":"/papers/arxiv/2505.07457/","summary":"Large Language Models (LLMs) have potential in mimicking human behavior in economic markets, but need more research for improved diversity and accuracy.","featured":"2025-05-14","label":"arXiv","topic":"ML & AI Methods","cites":19,"score":21,"scale":"shares"},{"title":"GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions","url":"/papers/arxiv/2505.05523/","summary":"A literature review identifies five key themes in the impact of Generative AI on entrepreneurship, calling for more broad-scale research and effective regulations.","featured":"2025-05-14","label":"arXiv","topic":"ML & AI Methods","cites":21,"score":16,"scale":"shares"},{"title":"Big Data and the Computational Social Science of Entrepreneurship and Innovation","url":"/papers/doi/10-1515-9783111085722-019/","summary":"The chapter highlights the potential of using large-scale data in entrepreneurship and innovation research, suggesting that machine-learning models and big data can create precision measurements and 'digital doubles' for virtual experimentation.","featured":"2025-05-14","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":13,"scale":"shares"},{"title":"Transfer Learning Across Fixed-Income Product Classes","url":"/papers/arxiv/2505.07676/","summary":"The paper proposes a framework for transferring learning of discount curves across different fixed-income product classes, enhancing kernel ridge regression and introducing a term that promotes curve smoothness, resulting in improved extrapolation performance and tighter confidence intervals.","featured":"2025-05-14","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":15,"scale":"shares"},{"title":"Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions","url":"/papers/arxiv/2505.04553/","summary":"The article presents a reinforcement learning framework for managing risk objectives. This is achieved through a specialized Actor-Critic algorithm and an auxiliary variable sampling method. The effectiveness of this approach is confirmed through simulation experiments in statistical arbitrage trading.","featured":"2025-05-14","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":19,"scale":"shares"},{"title":"A Survey on Prevention of Cyber-Attacks in Cloud Environment Using Machine Learning Techniques","url":"/papers/ssrn/5237733/","summary":"The study assesses the use of machine learning in improving IoT cybersecurity in Colombia and cloud computing, discussing the pros and cons of different algorithms.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Reverse Classification Challenge","url":"/papers/ssrn/5237861/","summary":"The article suggests a new method for experimental research, using machine learning metrics to evaluate results, potentially enhancing their reliability.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"AI and ML for Educational Reform","url":"/papers/ssrn/5238469/","summary":"The literature review discusses how artificial intelligence and machine learning can advance social justice and educational reform, highlighting the need for data privacy and equal technology access policies.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Predicting Laser Fabrication","url":"/papers/ssrn/5240746/","summary":"The study uses machine learning to predict the properties of glass optical diffusers made by indirect laser machining, proving the effectiveness of combining this technique with machine learning models.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AGNOSTIC for Quantitative Finance","url":"/papers/ssrn/5242085/","summary":"The AGNOSTIC tool, using online learning and convex optimization, helps overcome dimensionality and overfitting issues in Quantitative Finance without depending on assumptions or models.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Neural Network Asset Return Prediction","url":"/papers/ssrn/5237873/","summary":"Applying Fourier series expansion to the average asset return function can help solve the equity premium puzzle by capturing both sine and cosine components.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Legal Authority for AI Development","url":"/papers/ssrn/5231319/","summary":"The paper introduces a risk-based AI governance framework from the Philippines, mapping AI-related risks to existing Philippine laws.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Data Preprocessing in Machine Learning for Intrusion Detection","url":"/papers/ssrn/5241031/","summary":"The study emphasizes the importance of data preprocessing in improving the performance and reliability of machine learning algorithms used for intrusion detection.","featured":"2025-05-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?","url":"/papers/arxiv/2505.02846/","summary":"The article proposes a 'wait-and-monitor' strategy, using regulatory sandboxes, to balance risk and innovation in AI governance.","featured":"2025-05-07","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":14,"scale":"shares"},{"title":"Game Theory and Multi-Agent Reinforcement Learning for Zonal Ancillary Markets","url":"/papers/arxiv/2505.03288/","summary":"The study uses game theory to analyze market coupling, finding that multi-agent deep reinforcement learning reduces market costs but increases profit variability.","featured":"2025-05-07","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":12,"scale":"shares"},{"title":"Who Gets the Callback? Generative AI and Gender Bias","url":"/papers/arxiv/2504.21400/","summary":"Research shows that generative AI used in hiring processes tends to favor male candidates, particularly for high-paying roles, suggesting a gender bias.","featured":"2025-05-07","label":"arXiv","topic":"ML & AI Methods","cites":9,"score":50,"scale":"shares"},{"title":"AI Development and Job Market","url":"/papers/ssrn/5233820/","summary":"The research explores the effect of artificial intelligence on unemployment, predicting that AI will replace 85 million jobs by 2025 but also create over 95 million new ones.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"AI and ML in Future SOA Trends","url":"/papers/ssrn/5236155/","summary":"The article explores the future of Service-Oriented Architecture (SOA) enhanced by Artificial Intelligence (AI) and Machine Learning (ML) for improved decision-making and data processing.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning: Theory and Practice","url":"/papers/ssrn/5234601/","summary":"Theory and Practice: Machine Learning From Theory to Practice is a book that bridges the gap between theoretical and practical aspects of machine learning, with a focus on real-world examples and ethics.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"MACHINE LEARNING-BASED PREDICTIVE FINANCE MANAGEMENT IN SAP ERP","url":"/papers/ssrn/5228499/","summary":"The study discusses the use of machine learning in SAP ERP systems for predictive finance management, highlighting its effectiveness in detecting compliance violations and predicting financial trends.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"An Automated Hyperparameter Tuning Approach For Optimizing Machine Learning Model Performance","url":"/papers/ssrn/5234657/","summary":"The study suggests a new automated method for fine-tuning machine learning models using optimization techniques like Bayesian Optimization, Genetic Algorithms, and Reinforcement Learning.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"An Integrated Machine Learning Model for Predicting Customer Churn in a Telecommunications Application","url":"/papers/ssrn/5234665/","summary":"The research introduces a combined machine-learning model to predict customer turnover in the telecom sector, utilizing deep learning, ensemble learning, and feature engineering.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"ML Phishing Detection","url":"/papers/ssrn/5233231/","summary":"The paper discusses the use and effectiveness of machine learning in detecting phishing attacks, along with the challenges faced.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Universal Test for Model Fit","url":"/papers/ssrn/5229414/","summary":"The article offers an alternative to cross-validation in machine learning, proposing an interrogation-based method for optimal model calibration.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"High-Accuracy Stock Market Prediction Using Machine Learning Techniques","url":"/papers/ssrn/5235296/","summary":"The paper investigates advanced machine learning techniques for stock market prediction, concluding that deep learning models combined with sentiment features perform better than traditional methods.","featured":"2025-04-30","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"QuantBench: benchmarking AI methods for quantitative investment from a full pipeline perspective","url":"/papers/arxiv/2504.18600/","summary":"The article introduces QuantBench, a benchmark platform for AI in quantitative investment, designed to speed up progress in the field by providing a common evaluation ground and promoting collaboration.","featured":"2025-04-30","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":16,"scale":"shares"},{"title":"How much does context affect the accuracy of AI health advice?","url":"/papers/arxiv/2504.18310/","summary":"Research shows that the effectiveness of large language models in health communication varies based on language, topic, and source, highlighting the need for comprehensive multilingual validation before use.","featured":"2025-04-30","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":25,"scale":"shares"},{"title":"AI Recommendations and Non-instrumental Image Concerns","url":"/papers/arxiv/2504.19047/","summary":"The paper reveals that non-instrumental image concerns cause individuals to underuse AI recommendations, leading to disregarded AI advice and decreased task performance.","featured":"2025-04-30","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":12,"scale":"shares"},{"title":"Exploring AI-powered Digital Innovations from A Transnational Governance Perspective: Implications for Market Acceptance and Digital Accountability Accountability","url":"/papers/arxiv/2504.20215/","summary":"The study explores the use of the Technology Acceptance Model in AI innovations under a transnational governance system, suggesting ways to increase AI accountability and global market acceptance.","featured":"2025-04-30","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":12,"scale":"shares"},{"title":"Quantifying State Collapse Risk","url":"/papers/ssrn/5222366/","summary":"The study presents a machine learning-based FivePillar Framework to predict the risk of state collapse, aiding policymakers in taking preventive measures.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":9,"scale":"shares"},{"title":"Loss Optimization for Machine Learning","url":"/papers/ssrn/5223862/","summary":"The paper suggests new methods for predicting losses and optimizing machine learning solutions using various types of data.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Jet Noise Modeling for Military Aircraft","url":"/papers/ssrn/5222220/","summary":"The study develops a machine learning-based framework to predict military aircraft noise levels, with highly accurate models showing strong linear correlation.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Hate Speech Detection in Social Media","url":"/papers/ssrn/5226378/","summary":"The paper introduces a machine learning technique for automated detection of hate speech on social media to curb the spread of harmful content.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Materials Discovery Interpretation","url":"/papers/ssrn/5223411/","summary":"The article highlights the importance of integrating scientific interpretation in the process of discovering materials through machine learning.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Provenance Tracking for ML Systems","url":"/papers/ssrn/5226904/","summary":"The paper introduces yProv4ML, a framework designed to standardize the recording of provenance information in machine learning processes.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Financial Machine Learning Dynamics","url":"/papers/ssrn/5226168/","summary":"Advanced machine learning models are more effective in predicting ultrahighfrequency stock returns than simpler models, a study reveals.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"Generative AI in Capital Markets","url":"/papers/ssrn/5226562/","summary":"AI use in financial analysis on Seeking Alpha platform boosts productivity and liquidity for undercovered firms, but provides less information to capital market participants than human articles.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Do Machine Learning Models Need to Be Sector Experts?","url":"/papers/ssrn/5224253/","summary":"A Hybrid model incorporating industry membership outperforms other machine learning models in predicting industry-level returns, offering higher Sharpe ratios and lower risk.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":3,"scale":"shares"},{"title":"AI Impact on Investments","url":"/papers/ssrn/5224162/","summary":"AI technology adoption by mutual fund managers leads to superior returns and lower expenses, especially in discretionary funds.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"How Stock Market Participants Use Generative Artificial Intelligence: Evidence from User-Platform Interaction Data","url":"/papers/ssrn/5224596/","summary":"Chinese stock market participants use Generative AI to process investment information, with firm size, short-term performance, and media coverage being key factors.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":3,"scale":"shares"},{"title":"Trade-In Acquisition Modeling for Automotive Retail with AI","url":"/papers/ssrn/5211849/","summary":"Novosteer Technologies has created an AI model to improve lead generation and vehicle sourcing for dealerships in fluctuating retail markets.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"DisasterScope: A Multi-Disaster Prediction and Emergency Response Platform","url":"/papers/ssrn/5219629/","summary":"DisasterScope is an AI system that predicts disasters in real-time and offers emergency assistance using open-source data and machine learning.","featured":"2025-04-23","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"The Paradox of Professional Input: How Expert Collaboration with AI Systems Shapes Their Future Value","url":"/papers/arxiv/2504.12654/","summary":"The paper discusses the paradox of professional expertise and AI, indicating that while automating professional roles may pose risks, it also presents opportunities for expertise evolution and new professional value.","featured":"2025-04-23","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":13,"scale":"shares"},{"title":"AI Safety Should Prioritize the Future of Work","url":"/papers/arxiv/2504.13959/","summary":"The paper emphasizes the often ignored impact of AI on future work, suggesting extensive transition support for meaningful work and advocating for a worker-friendly global AI governance framework to promote shared wealth and economic fairness.","featured":"2025-04-23","label":"arXiv","topic":"ML & AI Methods","cites":17,"score":19,"scale":"shares"},{"title":"The Formation of Production Networks: How Supply Chains Arise from Simple Learning with Minimal Information","url":"/papers/arxiv/2504.16010/","summary":"A new model allows firms to determine their selling price, production volume, and inputs, leading to a flexible production network that can adapt to shocks.","featured":"2025-04-23","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":10,"scale":"shares"},{"title":"AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis","url":"/papers/arxiv/2502.02555/","summary":"Multimodal Attention for MRI Synthesis: The study proposes AAD-DCE, a generative adversarial network for creating Dynamic Contrast-Enhanced MRI images, showing its superior performance compared to other DCE-MRI synthesis methods.","featured":"2025-04-23","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":6,"scale":"shares"},{"title":"Next-Generation Deep Learning Techniques for Enhanced Handwritten Recognition: A Study","url":"/papers/ssrn/5218364/","summary":"Despite progress in deep learning, challenges remain in Handwritten Text Recognition (HTR) due to varying handwriting styles and complexities like cursive writing and irregular spacing.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":5,"scale":"shares"},{"title":"Machine Learning for Finance Risk Assessment","url":"/papers/ssrn/5215754/","summary":"The article discusses the use of machine learning for risk assessment and valuation in the financial sector, emphasizing its potential to enhance risk management.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Predictive Maintenance in QAD ERP","url":"/papers/ssrn/5218658/","summary":"The research focuses on the use of machine learning for predictive maintenance in the QAD ERP system, aiming to minimize downtime and optimize equipment use.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"RESEARCH PAPER ON JET GENIE","url":"/papers/ssrn/5216042/","summary":"JetGenie, an AI-based conversational system, is designed to simplify airline ticket booking through intelligent automation and real-time support, providing a personalized solution for modern travelers.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"AI Algebra for Quantitative Finance","url":"/papers/ssrn/5212863/","summary":"The research presents a tensor-based framework that expands AI's algebraic foundations to quantitative finance, proving its effectiveness in simulating a portfolio of systematic investment strategies.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Private Linear Equation Solving in Federated Learning","url":"/papers/ssrn/5218783/","summary":"The paper suggests a method to disguise input data in federated learning to prevent information leakage, ensuring the modifications do not change the solutions.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Cloud-Based Data Integration & Machine Learning in Biopharma Supply Chain Optimization","url":"/papers/ssrn/5215742/","summary":"The study compares the effectiveness of five machine learning classifiers in detecting wormhole attacks in IoT networks.","featured":"2025-04-16","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Predicting Children's Travel Modes for School Journeys in Switzerland: A Machine Learning Approach Using National Census Data","url":"/papers/arxiv/2504.09947/","summary":"A machine learning study identifies distance as the key factor influencing how children in Switzerland travel to school.","featured":"2025-04-16","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":11,"scale":"shares"},{"title":"ID Algorithm for Machine Learning Decision Making","url":"/papers/ssrn/5207811/","summary":"The study shows that the ID3 algorithm combined with decision tree models can improve decision-making in machine learning.","featured":"2025-04-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning Antenna for 5G","url":"/papers/ssrn/5206710/","summary":"The study introduces a machine learning-based antenna for 5G and IEEE 802.11baBe applications, offering a wide tuning range and total spectrum.","featured":"2025-04-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Optimizing Netcdf Data Queries","url":"/papers/ssrn/5203998/","summary":"NetCDFaster, a web-based platform with a lightweight machine learning model, significantly speeds up data retrieval from large NetCDF datasets.","featured":"2025-04-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Physics-Informed Neural Network for Building Control","url":"/papers/ssrn/5203315/","summary":"The research introduces a Physics-Informed Modularized Neural Network for building energy modeling, addressing integration of physics priors and effectiveness of physics constraints.","featured":"2025-04-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Weighted Sparse Regression for Feature Selection","url":"/papers/ssrn/5204899/","summary":"The paper suggests a Weighted Sparse Regression with Mutual Information model for feature selection in high-dimensional data, improving learning accuracy and result comprehensibility.","featured":"2025-04-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement","url":"/papers/arxiv/2504.04300/","summary":"The paper presents a computational framework that uses deep reinforcement learning to solve financial market equilibria, providing predictions on asset returns and volatilities.","featured":"2025-04-09","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":12,"scale":"shares"},{"title":"Are Generative AI Agents Effective Personalized Financial Advisors?","url":"/papers/arxiv/2504.05862/","summary":"Large AI language models can match human financial advisors but may lead to unsuitable investments due to struggle with conflicting needs, despite users often favoring extroverted AI personas.","featured":"2025-04-09","label":"arXiv","topic":"ML & AI Methods","cites":35,"score":14,"scale":"shares"},{"title":"DBOT: Artificial Intelligence for Systematic Long-Term Investing","url":"/papers/arxiv/2504.05639/","summary":"The paper introduces DBOT, an AI system that can value any publicly traded company like an expert, discussing its potential impact on the financial industry and human analysts.","featured":"2025-04-09","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":13,"scale":"shares"},{"title":"Unraveling Human-AI Teaming: A Review and Outlook","url":"/papers/arxiv/2504.05755/","summary":"The study examines the transition of AI agents from passive tools to active collaborators, highlighting the need for better alignment with human values and objectives, and suggesting a research outlook on human-AI collaboration.","featured":"2025-04-09","label":"arXiv","topic":"ML & AI Methods","cites":22,"score":13,"scale":"shares"},{"title":"Masked Autoencoders Are Effective Tokenizers for Diffusion Models","url":"/papers/arxiv/2502.03444/","summary":"Tokenizers for Diffusion Models: The study presents MAETok, an autoencoder for latent diffusion models, which enhances the quality of high-resolution image synthesis by learning a semantically rich latent space.","featured":"2025-04-09","label":"Machine learning","topic":"ML & AI Methods","cites":94,"score":38,"scale":"shares"},{"title":"Brief analysis of DeepSeek R1 and its implications for Generative AI","url":"/papers/arxiv/2502.02523/","summary":"Generative AI Implications: The report covers the launch of DeepSeek's new reasoning model, DeepSeekR1, its technical progress, and its impact on Generative AI, despite the US's GPU export ban.","featured":"2025-04-09","label":"Machine learning","topic":"ML & AI Methods","cites":43,"score":26,"scale":"shares"},{"title":"ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation","url":"/papers/arxiv/2502.00893/","summary":"Open-Source Humanoid Platform for Loco-Manipulation: ToddlerBot is a low-cost, open-source humanoid robot platform for scalable policy learning and research in robotics and AI, enabling zero-shot policy transfer.","featured":"2025-04-09","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":53,"scale":"shares"},{"title":"NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild","url":"/papers/arxiv/2410.02907/","summary":"Unsupervised Learning of Browser Agents: NNetNav is a method for unsupervised interaction with websites, generating synthetic demonstrations for training browser agents and making the search more tractable.","featured":"2025-04-09","label":"Machine learning","topic":"ML & AI Methods","cites":57,"score":29,"scale":"shares"},{"title":"Competition Policy in GenAI & Copyright","url":"/papers/ssrn/5201510/","summary":"The article discusses the conflict between copyright protection and the use of copyrighted works for AI training, suggesting a need for policy decisions on copyright holder compensation.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":9,"scale":"shares"},{"title":"Machine Learning for Na-Ion Battery Electrodes","url":"/papers/ssrn/5200529/","summary":"The research uses the Materials Genome Initiative and machine learning to enhance the performance of sodium-ion battery electrode materials and improve machine learning efficiency.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optimizing Workflows with Neural Networks","url":"/papers/ssrn/5195894/","summary":"The article discusses a hybrid approach to optimizing work processes by transferring some tasks to neural networks and solving the rest using alternative methods, leading to reduced energy costs and improved efficiency.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AIPowered Replication of PE Funds","url":"/papers/ssrn/5199100/","summary":"A new framework is introduced for replicating private equity performance using liquid AI-enhanced strategies, offering a liquid, scalable solution that aligns closely with traditional quarterly PE benchmarks.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Regulation Accounting Careers","url":"/papers/ssrn/5197320/","summary":"The Sarbanes-Oxley Act inadvertently limited accountants' on-the-job learning opportunities, deterring top talent from the profession.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Learning about Discount Rates","url":"/papers/ssrn/5197518/","summary":"Company managers learn about risk and compensation from target stock prices in M&A deals, but not from cashflows, which they already comprehend.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Optimizing Hybrid Quantum-Classical Algorithms","url":"/papers/ssrn/5198544/","summary":"Hybrid algorithms that merge Quantum Machine Learning with classical machine learning can improve computational performance and accuracy.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Cloud-Based ML in Biopharmaceutical SC","url":"/papers/ssrn/5199156/","summary":"Cloud-based data integration and machine learning are improving the efficiency of biopharmaceutical supply chain operations.","featured":"2025-04-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model","url":"/papers/arxiv/2503.22192/","summary":"The research introduces a combined deep learning framework for stock price prediction, demonstrating its effectiveness and reliability in predicting stock price movements.","featured":"2025-04-02","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":15,"scale":"shares"},{"title":"The Limits of AI in Financial Services","url":"/papers/arxiv/2503.22035/","summary":"The EPOCH framework suggests that AI will not replace jobs but change them, emphasizing the need for professionals to adapt.","featured":"2025-04-02","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":14,"scale":"shares"},{"title":"QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search","url":"/papers/arxiv/2502.02584/","summary":"Language Agents Search: QLASS system enhances the efficiency of language agents by offering step-by-step guidance, improving decision-making in complex tasks.","featured":"2025-04-02","label":"Machine learning","topic":"ML & AI Methods","cites":19,"score":202,"scale":"shares"},{"title":"Particle trajectory representation learning with masked point modeling","url":"/papers/arxiv/2502.02558/","summary":"PoLAr-MAE is a self-supervised learning framework for 3D particle trajectory analysis in Time Projection Chambers, matching the performance of supervised baselines without labeled data.","featured":"2025-04-02","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":13,"scale":"shares"},{"title":"Learning the RoPEs: Better 2D and 3D Position Encodings with STRING","url":"/papers/arxiv/2502.02562/","summary":"The article introduces STRING, an extension of Rotary Position Encodings, which offers exact translation invariance and low computational footprint, proving beneficial in robotics and Vision Transformers.","featured":"2025-04-02","label":"Machine learning","topic":"ML & AI Methods","cites":20,"score":23,"scale":"shares"},{"title":"Calibrated Multi-Preference Optimization for Aligning Diffusion Models","url":"/papers/arxiv/2502.02588/","summary":"The article presents Calibrated Preference Optimization (CaPO), a method for aligning text-to-image diffusion models without human annotated data, outperforming previous methods.","featured":"2025-04-02","label":"Machine learning","topic":"ML & AI Methods","cites":44,"score":5,"scale":"shares"},{"title":"SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration","url":"/papers/arxiv/2501.01320/","summary":"Video Restoration with Diffusion Transformer: The article introduces SeedVR, a diffusion transformer for video restoration of any length and resolution, showing superior performance over existing methods for generic video restoration.","featured":"2025-04-02","label":"Machine learning","topic":"ML & AI Methods","cites":70,"score":8,"scale":"shares"},{"title":"Data Automation using ML","url":"/papers/ssrn/5190229/","summary":"ElectroDrawManager uses machine learning to streamline electrical engineering tasks, reducing manual work and enhancing decision-making.","featured":"2025-03-26","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":9,"scale":"shares"},{"title":"Tube Heat Transfer Prediction","url":"/papers/ssrn/5189205/","summary":"The paper compares models for predicting the heat transfer coefficient, including traditional correlations and machine learning methods, using a large experimental data set.","featured":"2025-03-26","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Modelling Human Cognition and Polarization using Reinforcement Learning","url":"/papers/ssrn/5190844/","summary":"The study investigates how confirmation bias leads to polarization, introducing a new belief formation model based on reinforcement learning and examining how sample size impacts polarization.","featured":"2025-03-26","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"Wireless Networks with ML","url":"/papers/ssrn/5193014/","summary":"The machine learning system uses the BAT computational approach and a neural network to minimize network redundancy and reduce energy use, extending sensor life.","featured":"2025-03-26","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI in Capital Markets: Cases and Risks","url":"/papers/ssrn/5179985/","summary":"Cases and Risks: AI has revolutionized capital markets but also poses challenges like ethical issues, regulatory uncertainty, and systemic risks, necessitating comprehensive regulatory measures.","featured":"2025-03-26","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Airline Crew Recovery","url":"/papers/ssrn/5180044/","summary":"The article proposes a solution that uses optimization and machine learning to fix disrupted airline crew schedules, considering aircraft recovery plans and passenger disruption costs.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Cricket Data Analytics: Building the Best XI Players from T20 World Cup","url":"/papers/ssrn/5180123/","summary":"The study uses a data analytics framework and machine learning to identify the best cricket players from the T20 World Cup 2022, improving decision-making in cricket analytics.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":3,"score":2,"scale":"shares"},{"title":"Household Energy Bill Prediction Using Various Machine Learning Techniques","url":"/papers/ssrn/5183320/","summary":"The research develops predictive solutions using machine learning to forecast electricity prices, focusing on affordability and the influence of economic policies.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Dynamic Building Properties","url":"/papers/ssrn/5179872/","summary":"The research shows that building height affects the natural frequency in predicting buildings' dynamic properties using machine learning, with taller buildings having a lower frequency.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Corruption and Innovation Analysis","url":"/papers/ssrn/5153635/","summary":"A study using machine learning reveals that corruption greatly hampers innovation and economic growth by reducing profits and devaluing patents.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Fake Reviews Detection on Amazon","url":"/papers/ssrn/5156231/","summary":"A new study suggests using a Graph Neural Network model to detect fake reviews on ecommerce platforms, improving detection accuracy by about 10%.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":42,"scale":"shares"},{"title":"FAULT-TOLERANT LOAD BALANCING IN CLOUD-BASED FINANCIAL ANALYTICS: A REINFORCEMENT LEARNING APPROACH","url":"/papers/ssrn/5152209/","summary":"Traditional load balancing methods have limitations in adjusting to fluctuating workloads and unexpected system failures, affecting performance in real-time financial analytics.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":15,"scale":"shares"},{"title":"AI and Human Skills Demand","url":"/papers/ssrn/5153230/","summary":"AI roles demand more complementary skills like resilience, agility, and analytical thinking, which command a significant wage premium, as per an analysis of 12 million US online job vacancies.","featured":"2025-03-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":97,"scale":"shares"},{"title":"HQNN-FSP: A hybrid classical-quantum neural network for regression-based financial stock market prediction","url":"/papers/arxiv/2503.15403/","summary":"The research investigates the use of hybrid quantum-classical methods for predicting financial trends, introducing a Quantum Neural Network (QNN) regressor and two hybrid optimization strategies.","featured":"2025-03-20","label":"arXiv","topic":"ML & AI Methods","cites":34,"score":13,"scale":"shares"},{"title":"Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning","url":"/papers/arxiv/2503.11270/","summary":"The study finds that Deep Reinforcement Learning algorithms are more effective in identifying collusion risks in pricing strategies, performing better than Tabular Q-learning.","featured":"2025-03-20","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":15,"scale":"shares"},{"title":"The impact of artificial intelligence technology on cross-border trade in Southeast Asia: A meta-analytic approach","url":"/papers/arxiv/2503.13529/","summary":"Adoption of artificial intelligence boosts trade volumes in Southeast Asia, especially in areas with advanced tech infrastructure and supportive regulations.","featured":"2025-03-20","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":14,"scale":"shares"},{"title":"The Deep Multi-FBSDE Method: A Robust Deep Learning Method for Coupled FBSDEs","url":"/papers/arxiv/2503.13193/","summary":"The paper introduces a new method for approximating complex mathematical equations, which proves to be more reliable than the standard method, even under challenging conditions.","featured":"2025-03-20","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":15,"scale":"shares"},{"title":"GATE: An Integrated Assessment Model for AI Automation","url":"/papers/arxiv/2503.04941/","summary":"AI Automation Impacts: The GATE model is presented, a dynamic tool that simulates the economic impacts of AI automation, enabling users to examine the effects of AI under various parameters and policy actions.","featured":"2025-03-12","label":"arXiv","topic":"ML & AI Methods","cites":14,"score":13,"scale":"shares"},{"title":"Optimizing ML Workflows","url":"/papers/ssrn/5140155/","summary":"SnowflakeDB's new framework enhances machine learning pipelines, effectively handling big data and scaling tasks.","featured":"2025-03-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Hyperparameter Tuning in Physics-Informed NN Architecture","url":"/papers/ssrn/5141458/","summary":"Improvements to the Physics-Informed Neural Networks (PINN) method optimize model performance by focusing on hyperparameter tuning.","featured":"2025-03-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":19,"scale":"shares"},{"title":"Solar prosumage under different pricing regimes: Interactions with the transmission grid","url":"/papers/arxiv/2502.21306/","summary":"Research shows that zonal pricing boosts solar energy investments in Germany, while fixed pricing discourages it, and regional solar availability significantly influences rooftop solar panel investments.","featured":"2025-03-05","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":15,"scale":"shares"},{"title":"Tracks to Modernity: Railroads, Growth, and Social Movements in Denmark","url":"/papers/arxiv/2502.21141/","summary":"A study on 19th-century Denmark reveals that railway expansion significantly boosted population growth and triggered institutional and cultural changes.","featured":"2025-03-05","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":11,"scale":"shares"},{"title":"Estimating Convex Production Technologies","url":"/papers/repec/eee-ejores-v-323-y-2025-i-1-p-224-240/","summary":"The research adapts Stochastic Gradient Boosting for Data Envelopment Analysis to estimate production possibility sets, reducing overfitting and satisfying shape constraints, as proven by simulations and a PISA example.","featured":"2025-03-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Improved xG Model for Football","url":"/papers/repec/taf-tjorxx-v-76-y-2025-i-1-p-1-13/","summary":"The study enhances the prediction performance of the expected goal model in football analytics by integrating data from various sources and using a supervised machine learning approach, resulting in significant improvements in sensitivity, F1 metrics, and AUC metric.","featured":"2025-03-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":10,"scale":"shares"},{"title":"Machine Learning for M&A","url":"/papers/repec/eee-finana-v-99-y-2025-i-c-s1057521925000201/","summary":"Machine learning models are more effective than traditional methods in predicting Chinese corporate merger and acquisition activities.","featured":"2025-03-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":28,"scale":"shares"},{"title":"AI Capability Firm Performance","url":"/papers/repec/spr-infosf-v-26-y-2024-i-6-d-10-1007-s10796-023-10460-z/","summary":"The research indicates that AI capability directly affects firm performance, with a data-driven culture and AI infrastructure playing key roles.","featured":"2025-03-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Dark Patterns in Retail","url":"/papers/repec/mul-jqmthn-doi-10-1435-115112-y-2024-i-3-p-499-531/","summary":"The article discusses the problem of dark patterns in retail investment and the potential of AI and behavioral sciences in enhancing regulation.","featured":"2025-03-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Financial Fraud Detection System Based on Improved Random Forest and Gradient Boosting Machine (GBM)","url":"/papers/arxiv/2502.15822/","summary":"The paper suggests a financial fraud detection system that uses an improved Random Forest and Gradient Boosting Machine model, offering an efficient and reliable solution for detecting financial fraud.","featured":"2025-02-26","label":"arXiv","topic":"ML & AI Methods","cites":5,"score":13,"scale":"shares"},{"title":"Algorithmic Collusion under Observed Demand Shocks","url":"/papers/arxiv/2502.15084/","summary":"Research indicates Q-learning agents can adjust pricing strategies and form tacit collusion in response to market conditions, managing prices during demand fluctuations.","featured":"2025-02-26","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":12,"scale":"shares"},{"title":"AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting","url":"/papers/arxiv/2502.16810/","summary":"The paper introduces a system that uses large language models to create persuasive, personalized real estate marketing content, which was favored over human-written descriptions in tests, indicating potential for automated, fact-based targeted marketing.","featured":"2025-02-26","label":"arXiv","topic":"ML & AI Methods","cites":10,"score":16,"scale":"shares"},{"title":"Harnessing AI and ML for Seamless Cloud Migration","url":"/papers/ssrn/5132909/","summary":"A review indicates that AI and machine learning can simplify the process of cloud migration, despite issues like shortage of skilled workers and data security risks.","featured":"2025-02-19","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":10,"scale":"shares"},{"title":"Material Models Measured Strain Fields","url":"/papers/ssrn/5116654/","summary":"The Onestep SelfSim method uses machine learning to create material models from measured strain fields, accurately capturing the elastoplastic behavior of materials.","featured":"2025-02-19","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Assessing Generative AI value in a public sector context: evidence from a field experiment","url":"/papers/arxiv/2502.09479/","summary":"The application of Generative AI in public sector tasks showed mixed results, improving document understanding but decreasing data analysis quality.","featured":"2025-02-19","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":6,"scale":"shares"},{"title":"Cracking the Code: Enhancing Development finance understanding with artificial intelligence","url":"/papers/arxiv/2502.09495/","summary":"Machine learning and natural language processing have been used to analyze the OECD's Creditor Reporting System dataset, revealing hidden aspects of development finance.","featured":"2025-02-19","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Could AI Leapfrog the Web? Evidence from Teachers in Sierra Leone","url":"/papers/arxiv/2502.12397/","summary":"Evidence from Sierra Leone Teachers: An AI chatbot used by teachers in Sierra Leone has proven to be more efficient and relevant than traditional web search, indicating AI's potential in areas with limited internet access.","featured":"2025-02-19","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":7,"scale":"shares"},{"title":"Generalized Factor Neural Network Model for High-dimensional Regression","url":"/papers/arxiv/2502.11310/","summary":"A novel method combining non-parametric regression, factor models, and neural networks has been introduced, showing effectiveness in predicting equity ETF indices prices and macroeconomic data.","featured":"2025-02-19","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":7,"scale":"shares"},{"title":"Stochastic Lot Streaming and Scheduling with Machine Learning","url":"/papers/repec/taf-uiiexx-v-57-y-2025-i-4-p-408-422/","summary":"The article proposes a new algorithm and machine learning model for the Lot Streaming and Scheduling Problem (LSSP) with uncertain product arrival times, aiming to enhance efficiency and precision.","featured":"2025-02-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Lessons from Social Media for Climate Policy","url":"/papers/repec/cup-nierev-v-266-y-2023-i-p-22-29-3/","summary":"The study uses machine learning to analyze social media discussions on climate change and suggests diverse policies for net-zero goals.","featured":"2025-02-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"AI Techniques for Cloud Resource Management","url":"/papers/repec/das-njaigs-v-6-y-2024-i-1-p-397-408-id-262/","summary":"The paper discusses the use of AI techniques to improve resource management in cloud environments, boosting DevOps workflows' performance and efficiency.","featured":"2025-02-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Strategic AI Governance in Moldova","url":"/papers/repec/awf-journl-y-2024-i-2-p-33-53/","summary":"The article suggests a framework for AI governance in Moldova to meet EU standards, highlighting the role of responsible AI governance in supporting Moldova's EU aspirations.","featured":"2025-02-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optical Logic Gates","url":"/papers/ssrn/5115354/","summary":"MLFOLD, a machine learning-driven approach, is proposed for designing and optimizing all-optical XOR, OR, and NOT logic gates on a single photonic crystal substrate.","featured":"2025-02-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment","url":"/papers/arxiv/2502.00151/","summary":"The article explores the use of deep neural networks in business decision making, particularly in financial prediction, suggesting a stronger framework can be created by combining multiple networks.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":9,"scale":"shares"},{"title":"Examining the Impact of Income Inequality and Gender on School Completion in Malaysia: A Machine Learning Approach Utilizing Malaysia's Public Sector Open Data","url":"/papers/arxiv/2501.18868/","summary":"The study uses machine learning to analyze the link between income inequality, gender, and school completion rates in Malaysia, revealing significant disparities and recommending targeted interventions.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Testing Capacity-Constrained Learning","url":"/papers/arxiv/2502.00195/","summary":"The paper presents a test of capacity-constrained learning models, finding that choice data aligns with these models if a No Improving Switches condition is met, and offering insights into how incentives affect attention levels.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":6,"scale":"shares"},{"title":"Can the Nexus of Scaling Laws Coupled with Constant or Variable Elasticity of Substitution Predict AI and Other Technology Adoption?","url":"/papers/arxiv/2502.00909/","summary":"The price drops and adoption rates of emerging technologies like solar power and AI are interconnected, aiding in the creation of more sophisticated technology adoption models.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Exploratory Utility Maximization Problem with Tsallis Entropy","url":"/papers/arxiv/2502.01269/","summary":"The study investigates the problem of maximizing utility in the reinforcement learning framework, revealing that excessive exploration can lead to ill-posedness in some cases, and suggests a reinforcement learning algorithm that highlights the benefits of reinforcement learning.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":5,"scale":"shares"},{"title":"Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review","url":"/papers/arxiv/2502.00201/","summary":"The study examines progress in deep learning methods for detecting financial fraud, reviewing 57 studies from 2019 to 2024, and discusses challenges and opportunities such as data privacy, feature engineering, and model interpretability.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":51,"score":3,"scale":"shares"},{"title":"AI Governance through Markets","url":"/papers/arxiv/2501.17755/","summary":"The article proposes combining market governance mechanisms with traditional regulations to incentivize responsible AI development.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":19,"scale":"shares"},{"title":"Progress in Artificial Intelligence and its Determinants","url":"/papers/arxiv/2501.17894/","summary":"The study shows exponential growth in AI through patents, publications, and machine learning benchmarks, emphasizing the importance of AI researchers.","featured":"2025-02-05","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":14,"scale":"shares"},{"title":"o3-mini vs DeepSeek-R1: Which One is Safer?","url":"/papers/arxiv/2501.18438/","summary":"DeepSeek-R1 vs o3-mini: The AI model DeepSeek-R1 has been found to produce more unsafe responses than OpenAI's o3-mini, according to a technical report using the ASTRAL testing tool.","featured":"2025-02-05","label":"Machine learning","topic":"ML & AI Methods","cites":35,"score":55,"scale":"shares"},{"title":"Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling","url":"/papers/arxiv/2501.18577/","summary":"A novel method has been introduced to provide valid confidence intervals when machine learning algorithms fill in missing variables, extending its use to nonuniform samples and various feature subsets.","featured":"2025-02-05","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":24,"scale":"shares"},{"title":"SOAP: Improving and Stabilizing Shampoo using Adam","url":"/papers/arxiv/2409.11321/","summary":"A new algorithm, SOAP, enhances the computational efficiency of the Shampoo preconditioning method in deep learning tasks, reducing iterations and time, with an online implementation available.","featured":"2025-02-05","label":"Machine learning","topic":"ML & AI Methods","cites":223,"score":197,"scale":"shares"},{"title":"Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate","url":"/papers/arxiv/2501.17703/","summary":"The article introduces Critique Fine-Tuning (CFT), a new method for training language models that critiques incorrect responses, showing better results than the traditional Supervised Fine-Tuning (SFT) method in math benchmarks.","featured":"2025-02-05","label":"Machine learning","topic":"ML & AI Methods","cites":63,"score":110,"scale":"shares"},{"title":"Brain-Inspired AI with Hyperbolic Geometry","url":"/papers/arxiv/2409.12990/","summary":"The paper suggests that using hyperbolic geometry in artificial neural networks (ANNs) and machine learning, inspired by the human brain's structure, could improve accuracy and efficiency in various tasks.","featured":"2025-02-05","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":55,"scale":"shares"},{"title":"Table Tennis Network Metrics","url":"/papers/repec/eee-chsofr-v-191-y-2025-i-c-s0960077924014450/","summary":"The research uses machine learning to predict table tennis game outcomes based on new technical-tactical style metrics, demonstrating superior predictive accuracy.","featured":"2025-02-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Random Forest Choice Models","url":"/papers/repec/spr-empeco-v-68-y-2025-i-1-d-10-1007-s00181-024-02646-4/","summary":"The paper introduces the Ordered Forest, a new machine learning estimator for ordered choice models, which estimates conditional choice probabilities and marginal effects.","featured":"2025-02-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Machine Learning for Sales Prediction","url":"/papers/repec/bjf-journl-v-9-y-2024-i-12-p-623-628/","summary":"Machine learning, specifically gradient boosting, can accurately predict e-commerce sales, influenced by pricing, promotions, and seasonal factors.","featured":"2025-02-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Hybrid Multiaxial Fatigue Life Prediction","url":"/papers/ssrn/5101380/","summary":"A new fatigue life prediction model, combining physical constraints and machine learning, outperforms other models.","featured":"2025-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Math of Schmidhuber Architectures","url":"/papers/ssrn/5103305/","summary":"The article reviews key architectures by Jürgen Schmidhuber, emphasizing his significant contributions to deep learning and artificial intelligence.","featured":"2025-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":23,"scale":"shares"},{"title":"Coalbed Methane Production Prediction","url":"/papers/ssrn/5100377/","summary":"The study introduces a hybrid machine learning model for predicting coalbed methane production, which provides more accurate predictions.","featured":"2025-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Lightweight Deep Learning for Tower Asset Classification","url":"/papers/ssrn/5101612/","summary":"The research proposes a new framework for inspecting telecommunication towers using drones with LiDAR sensors.","featured":"2025-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"NVIDIA's Cosmos Models","url":"/papers/ssrn/5088015/","summary":"The paper offers a technical analysis of NVIDIA's Cosmos World Foundation Model Platform for Physical AI, highlighting its architecture, training methods, and performance.","featured":"2025-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":45,"scale":"shares"},{"title":"Mathematical Generative Modeling Framework","url":"/papers/ssrn/5089573/","summary":"The article introduces a mathematical model that combines five major generative modeling paradigms, using optimal transport theory, stochastic differential equations, and information geometry.","featured":"2025-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":26,"scale":"shares"},{"title":"Physics of Skill Learning","url":"/papers/arxiv/2501.12391/","summary":"The study proposes three models - Geometry, Resource, and Domino - to understand the physics of skill learning in neural networks, offering insights into neural scaling laws and learning dynamics.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":260,"scale":"shares"},{"title":"Learning Segmentation from Point Trajectories","url":"/papers/arxiv/2501.12392/","summary":"The study introduces a method for segmenting objects in videos based on motion, using long-term point trajectories to complement optical flow, improving motion-based segmentation.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":14,"score":30,"scale":"shares"},{"title":"Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists","url":"/papers/arxiv/2501.12374/","summary":"A study shows that while AI tools can assist in artistic creation, professional artists still produce more creative and accurate work, though the difference is slight.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":13,"scale":"shares"},{"title":"DexForce: Extracting Force-Informed Actions From Kinesthetic Demonstrations for Dexterous Manipulation","url":"/papers/arxiv/2501.10356/","summary":"DexForce, a new method for capturing demonstrations of complex manipulation, uses contact forces to compute actions for policy learning, achieving a 76% success rate.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":55,"score":10,"scale":"shares"},{"title":"Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps","url":"/papers/arxiv/2501.09732/","summary":"The research shows that increasing computation during inference-time can enhance the quality of samples produced by diffusion models, especially in image generation.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":256,"score":175,"scale":"shares"},{"title":"Learnings from Scaling Visual Tokenizers for Reconstruction and Generation","url":"/papers/arxiv/2501.09755/","summary":"The study reveals that scaling the decoder in auto-encoders, specifically the VisionTransformer architecture for Tokenization (ViTok), improves reconstruction performance and sets new standards for class-conditional video generation when combined with Diffusion Transformers.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":33,"score":31,"scale":"shares"},{"title":"T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation","url":"/papers/arxiv/2407.14505/","summary":"Text-to-Video Benchmark: TV-CompBench, a new benchmark for evaluating text-to-video generative models, shows that current models struggle with composing various elements into a video.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":183,"score":28,"scale":"shares"},{"title":"Neuradicon: Operational representation learning of neuroimaging reports","url":"/papers/arxiv/2107.10021/","summary":"Learning Neuroimaging Reports: Neuradicon, a new natural language processing framework, has been developed for analyzing neuroradiological reports, showing excellent adaptability across different time periods and healthcare institutions.","featured":"2025-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":26,"scale":"shares"},{"title":"Feature Importance in Financial Models","url":"/papers/repec/eee-finlet-v-71-y-2025-i-c-s1544612324014351/","summary":"Machine Learning can produce misleading results in financial models that assume linearity, indicating the need for careful application.","featured":"2025-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":18,"scale":"shares"},{"title":"Determinants of Zero-Leverage","url":"/papers/repec/eee-finlet-v-71-y-2025-i-c-s154461232401345x/","summary":"The study uses machine learning to identify factors influencing the zero-leverage phenomenon, including cash holdings, tangible assets, industry leverage-level, and firm size, and suggests a solution for sample imbalance.","featured":"2025-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Improving Location Analytics with Explainable AI","url":"/papers/repec/taf-rjerxx-v-46-y-2024-i-4-p-421-443/","summary":"The paper presents the SHAP location score, a new data-based method for assessing real estate locations, enhancing traditional urban models and benefiting real estate stakeholders.","featured":"2025-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":12,"scale":"shares"},{"title":"Estimating Location Value with SHAP","url":"/papers/ssrn/5089118/","summary":"The study uses AI to calculate the location value of Swiss apartments, distinguishing it from the building's value and comparing across different areas.","featured":"2025-01-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Decentralized Diffusion Models","url":"/papers/arxiv/2501.05450/","summary":"The paper suggests Decentralized Diffusion Models, a framework for distributing AI model training across separate clusters, reducing costs and increasing resilience to GPU failures.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":14,"score":80,"scale":"shares"},{"title":"The GAN is dead; long live the GAN! A Modern GAN Baseline","url":"/papers/arxiv/2501.05441/","summary":"The study introduces R3GAN, a simplified GAN baseline that outperforms StyleGAN2 on various datasets and competes well against other state-of-the-art GANs and diffusion models.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":110,"score":67,"scale":"shares"},{"title":"GenMol: A Drug Discovery Generalist with Discrete Diffusion","url":"/papers/arxiv/2501.06158/","summary":"Drug Discovery Generalist: The paper presents GenMol, a molecular generative model that surpasses previous models in new generation and fragment-constrained generation, offering a unified approach for drug discovery tasks.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":58,"score":47,"scale":"shares"},{"title":"Neuro-Symbolic AI in 2024: A Systematic Review","url":"/papers/arxiv/2501.05435/","summary":"Neuro-Symbolic AI has grown since 2020, focusing on learning and inference, but still lacks in areas like explainability, trustworthiness, and Meta-Cognition.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":91,"score":40,"scale":"shares"},{"title":"Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control","url":"/papers/arxiv/2409.08861/","summary":"The study presents Adjoint Matching, a new algorithm that enhances dynamical generative models by refining reward fine-tuning, leading to improved consistency, realism, and adaptability to unseen human preference reward models.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":227,"score":222,"scale":"shares"},{"title":"Grokking at the Edge of Numerical Stability","url":"/papers/arxiv/2501.04697/","summary":"The study investigates 'grokking' in deep learning, introduces Softmax Collapse and naïve loss minimization concepts, and suggests a new activation function and training algorithm for grokking without regularization.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":41,"score":50,"scale":"shares"},{"title":"Unity by Diversity: Improved Representation Learning in Multimodal VAEs","url":"/papers/arxiv/2403.05300/","summary":"A new mixture-of-experts prior for Variational Autoencoders for multimodal data has been proposed, replacing hard constraints with a soft one, leading to better latent representation and improved imputation of missing data modalities.","featured":"2025-01-15","label":"Machine learning","topic":"ML & AI Methods","cites":20,"score":41,"scale":"shares"},{"title":"Finance Research Trends from Machine Learning","url":"/papers/repec/wly-intsec-v-19-y-2024-i-4-p-472-507/","summary":"The paper employs machine learning models to identify trends in finance research topics from 1976 to 2015, revealing growth and shrinkage in topics and a consistent pattern in topic coverage among researchers.","featured":"2025-01-15","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Detection of Fake Online Review using Machine Learning","url":"/papers/ssrn/5086640/","summary":"The article highlights the role of online reviews in customer decision-making, the potential for supplier abuse, and the use of machine learning to identify genuine reviews.","featured":"2025-01-08","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":4,"scale":"shares"},{"title":"Multi-Task DL for Pavement Prediction","url":"/papers/ssrn/5082558/","summary":"The study creates a multitask deep learning approach to predict lane-level pavement performance using historical data, tested with a real case in China.","featured":"2025-01-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Geometric Data Analysis","url":"/papers/ssrn/5082309/","summary":"The article introduces GeomTop, a neural architecture that combines geometric convolutions with persistence-based features for data analysis in molecular dynamics and materials science.","featured":"2025-01-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":31,"scale":"shares"},{"title":"QLearning and HJB","url":"/papers/ssrn/5083336/","summary":"The article explores the relationship between Q-learning and the HamiltonJacobiBellman equation, discussing the conditions needed for the existence, uniqueness, and stability of viscosity solutions and Q-learning's convergence.","featured":"2025-01-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Stability of Deep Learning","url":"/papers/ssrn/5071672/","summary":"The stability of deep neural nets is examined from a weak dependence perspective, suggesting a new loss function for improved stability.","featured":"2025-01-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using 10-K Filings","url":"/papers/doi/10-1016-j-techfore-2024-123965/","summary":"A new method using natural language processing (NLP) has been suggested for classifying AI stocks, providing a cost-effective alternative that outperforms existing AI-themed ETFs.","featured":"2025-01-08","label":"arXiv","topic":"ML & AI Methods","cites":17,"score":20,"scale":"shares"},{"title":"R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization","url":"/papers/arxiv/2501.01421/","summary":"The study presents a new visual localization method using a covisibility graph-based global encoding learning and data augmentation strategy, achieving top results on large-scale datasets without needing network ensembles or 3D supervision.","featured":"2025-01-08","label":"Machine learning","topic":"ML & AI Methods","cites":28,"score":24,"scale":"shares"},{"title":"Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity","url":"/papers/arxiv/2501.03203/","summary":"The research introduces tools for detecting AI-generated content in student work using machine learning and deep learning algorithms, aiming to uphold academic integrity and responsible AI use in education.","featured":"2025-01-08","label":"Machine learning","topic":"ML & AI Methods","cites":16,"score":14,"scale":"shares"},{"title":"Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models","url":"/papers/arxiv/2501.01423/","summary":"The paper proposes a new model, VA-VAE, that aligns the latent space with pre-trained vision foundation models, enabling faster convergence of Diffusion Transformers in high-dimensional latent spaces and achieving top performance on ImageNet 256x256 generation.","featured":"2025-01-08","label":"Machine learning","topic":"ML & AI Methods","cites":395,"score":14,"scale":"shares"},{"title":"AI in Global Higher Ed","url":"/papers/repec/igg-jismd0-v-16-y-2025-i-1-p-1-24/","summary":"Research shows China, the US, and England are leading in AI education research, with future trends including AI-VR integration, sentiment analysis, and predictive student performance models.","featured":"2025-01-08","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Math Equivalence of Decision Trees & Neural Networks","url":"/papers/ssrn/5074559/","summary":"The article presents a mathematical proof showing the similarity between decision trees and artificial neural networks, hinting at new combined machine learning methods.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":37,"scale":"shares"},{"title":"ML in Financial Analytics","url":"/papers/ssrn/5077670/","summary":"The research assesses the impact of Machine Learning in finance, emphasizing its potential in personal finance optimization, but also noting issues like data privacy and algorithmic biases.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":30,"scale":"shares"},{"title":"Kolmogorov-Arnold-Networks vs ANN","url":"/papers/ssrn/5072413/","summary":"The paper mathematically compares Kolmogov-Arnold-Networks and traditional Artificial Neural Networks, showing a balance between expressivity and computational complexity.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":81,"scale":"shares"},{"title":"AI Big Data in Forensic Accounting","url":"/papers/ssrn/5078259/","summary":"Forensic accounting is being revolutionized by AI and Big Data analytics, improving fraud detection and investigation, but adoption is hindered by high costs, privacy issues, and a lack of technical expertise.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Wake Area Prediction with DMD & ML","url":"/papers/ssrn/5073355/","summary":"A new framework combining Machine Learning and Dynamic Mode Decomposition has been introduced to enhance flow prediction and control in vortex-induced vibration systems.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Big Data & AI for Financial Fraud Detection","url":"/papers/ssrn/5076886/","summary":"Financial fraud detection is being transformed by big data analytics, machine learning, and natural language processing, which can identify patterns and anomalies often missed by traditional systems.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Rapid Copper Alloy Design","url":"/papers/ssrn/5078544/","summary":"A proposed alloy design strategy combines orthogonal experiments, machine learning, and Pareto analysis to address the challenge of using data-driven methods with limited sample data.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Dataset Distillation with Stochastic NN","url":"/papers/ssrn/5077187/","summary":"The use of stochastic neural networks and uncertainty estimation is proposed to improve the quality and speed of dataset distillation.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Online Semi-Supervised SVM","url":"/papers/ssrn/5076855/","summary":"A new online algorithm, OS2ISVM, has been introduced to enhance the efficiency of SVMs in online semi-supervised learning scenarios.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI Investment Decisions","url":"/papers/ssrn/5075727/","summary":"AI enhances investment performance, but it benefits those with greater financial expertise more, potentially increasing existing performance disparities.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"AI in Finance","url":"/papers/ssrn/5061691/","summary":"The review discusses the use of Artificial Intelligence in financial markets, including trading, fraud detection, and credit scoring, and addresses data privacy and regulatory issues.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":10,"scale":"shares"},{"title":"RL Transformers","url":"/papers/ssrn/5064791/","summary":"The paper analyzes the integration of Transformer architectures with Reinforcement Learning, offering a mathematical framework, a new taxonomy, and a review of real-world applications.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":62,"scale":"shares"},{"title":"ML Takeover Verification","url":"/papers/ssrn/5067354/","summary":"The study uses machine learning models to predict the accuracy of corporate takeover rumors, emphasizing the effectiveness of TabNet and the need for data imbalance and dimensionality reduction.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Learning Strategies in Broker-Mediated Markets","url":"/papers/ssrn/5070540/","summary":"The article reveals that brokers in a broker-mediated market have a strategic advantage and can profit from information leakage in client trading.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Predictive Analytics Optimization with PySpark","url":"/papers/ssrn/5075644/","summary":"The article talks about the integration of PySpark and machine learning models on Databricks to improve predictive analytics, and offers optimization strategies.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Distributed Architectures for Machine Learning","url":"/papers/ssrn/5076116/","summary":"The article discusses different architectures for distributed machine learning systems, their scalability potential, and design principles for enhanced scalability.","featured":"2025-01-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Complement or substitute? How AI increases the demand for human skills","url":"/papers/arxiv/2412.19754/","summary":"The paper shows that AI has increased demand for complementary skills and decreased demand for substitute skills in the U.S. economy.","featured":"2025-01-01","label":"arXiv","topic":"ML & AI Methods","cites":27,"score":11,"scale":"shares"},{"title":"Can AI help with your personal finances?","url":"/papers/arxiv/2412.19784/","summary":"The study assesses the accuracy of Large Language Models like OpenAI's ChatGPT and Google's Gemini in giving financial advice, finding a 70% accuracy rate but limitations with complex queries.","featured":"2025-01-01","label":"arXiv","topic":"ML & AI Methods","cites":17,"score":15,"scale":"shares"},{"title":"The Value of AI-Generated Metadata for UGC Platforms: Evidence from a Large-scale Field Experiment","url":"/papers/arxiv/2412.18337/","summary":"An experiment on an Asian short-video platform found that AI-generated titles boosted content consumption, but were inferior in quality to those created by humans, emphasizing the value of human-AI collaboration.","featured":"2025-01-01","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":9,"scale":"shares"},{"title":"Symbolic approximations to Ricci-flat metrics via extrinsic symmetries of Calabi–Yau hypersurfaces","url":"/papers/arxiv/2412.19778/","summary":"The paper uses machine learning to explore flat metrics of Fermat Calabi-Yau n-folds, revealing new properties and achieving significant reductions in Ricci curvature.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":11,"score":9,"scale":"shares"},{"title":"IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring Distribution-Aware Data Reshaping","url":"/papers/arxiv/2412.19750/","summary":"The paper introduces IMAGINE, a compute-in-memory SRAM for processing convolutional neural networks, which offers high energy efficiency and competitive accuracies on MNIST and CIFAR-10.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":8,"scale":"shares"},{"title":"Tensor Network Estimation of Distribution Algorithms","url":"/papers/arxiv/2412.19780/","summary":"The study explores the use of tensor networks in evolutionary optimization algorithms, concluding that better generative models don't always improve optimization performance and suggests adding a mutation operator for better results.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":8,"scale":"shares"},{"title":"Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?","url":"/papers/arxiv/2407.21792/","summary":"Research suggests AI safety benchmarks often align with general capabilities and training compute, leading to potential safetywashing, and recommends a stricter framework for AI safety research.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":85,"score":178,"scale":"shares"},{"title":"Keypoint Aware Masked Image Modelling","url":"/papers/arxiv/2407.13873/","summary":"KAMIM is a new method that enhances vision transformers' performance by using patch-wise weighting from keypoint features, showing improved accuracy on the ImageNet-1K dataset.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":12,"scale":"shares"},{"title":"Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data","url":"/papers/arxiv/2407.10854/","summary":"A new computational technique has been introduced for modeling the evolution of dynamical systems, specifically targeting the complex problem of modeling partially-observed partial differential equations on high-dimensional non-uniform grids.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":4,"score":11,"scale":"shares"},{"title":"DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation","url":"/papers/arxiv/2412.18597/","summary":"Attention Control in Video Generation: DiTCtrl is a new training-free method for multi-prompt video generation under MM-DiT architectures, allowing for mask-guided precise semantic control across different prompts.","featured":"2025-01-01","label":"Machine learning","topic":"ML & AI Methods","cites":81,"score":10,"scale":"shares"},{"title":"Warranty Accuracy: Machine Learning vs Human Estimates","url":"/papers/ssrn/5056018/","summary":"Machine Learning vs Human Estimates: Machine learning models have been found to be more accurate than human experts in predicting warranty provisions in accounting due to human errors like aggregation bias and historical cost anchoring.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Data Optimization for Large-Scale Industries","url":"/papers/ssrn/5058737/","summary":"The article explores the use of machine learning models and cloud environments to optimize the processing of telemetry data in massively multiplayer gaming platforms.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Enhanced Hybrid IDS for Zero-Day Attacks","url":"/papers/ssrn/5055229/","summary":"A hybrid approach combining deep learning and machine learning is suggested for detecting common cyberattacks in computer network intrusion detection systems.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Topological Quality Using Persistence Matching Diagrams","url":"/papers/ssrn/5059197/","summary":"The article suggests that the quality of a dataset for machine learning models can be gauged and the performance of a supervised learning model can be predicted using topological data analysis techniques.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Active Flow Control for Confined Square Cylinder Wake","url":"/papers/ssrn/5059657/","summary":"The article introduces a deep learning surrogate model-based reinforcement learning approach for active control of two-dimensional wake flow, which reduces computational costs while maintaining reliability.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Generative AI in Financial Info Processing","url":"/papers/ssrn/5053905/","summary":"The paper shows that nearly half of retail investors use generative AI for financial information processing, with more advanced investors utilizing it more effectively.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI 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affordable.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Quantum Computing Threats to Encrypted ML","url":"/papers/ssrn/5058511/","summary":"The paper discusses the potential threats of quantum computing to encrypted machine learning systems, highlighting the need for quantum-resistant encryption.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"INTERNATIONAL JOURNAL OF ADVANCES IN ENGINEERING RESEARCH AI for Proactive Data Quality Assurance: Enhancing Data Integrity and Reliability 1","url":"/papers/ssrn/5047707/","summary":"The study introduces an AI-based framework for proactive data quality assurance, showing its effectiveness in enhancing data integrity and reliability.","featured":"2024-12-18","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics","url":"/papers/arxiv/2412.12438/","summary":"The project uses a blend of technical, market, and statistical factors with machine learning to predict stock market performance, emphasizing the benefits of merging financial expertise with computational tools.","featured":"2024-12-18","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"Multiplexing in networks and diffusion","url":"/papers/arxiv/2412.11957/","summary":"The study finds that multiplex social and economic networks can slow the spread of simple information but can either hinder or boost the spread of complex information, affecting inequality in outcomes.","featured":"2024-12-18","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":13,"scale":"shares"},{"title":"BINARY OR NONBINARY? AN EVOLUTIONARY LEARNING APPROACH TO GENDER IDENTITY","url":"/papers/arxiv/2412.10959/","summary":"The analysis suggests that nonbinary gender identity could become dominant due to its adaptability, using a game-based approach and a genetic learning algorithm to study evolutionary dynamics.","featured":"2024-12-18","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate","url":"/papers/arxiv/2412.11257/","summary":"The Prediction-Enhanced Monte Carlo framework uses machine learning to enhance the efficiency of Monte Carlo simulations, especially in large, path-dependent problems.","featured":"2024-12-18","label":"arXiv","topic":"ML & AI Methods","cites":5,"score":11,"scale":"shares"},{"title":"VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback","url":"/papers/arxiv/2409.18417/","summary":"An auction mechanism is introduced to enhance cost-efficiency in fine-tuning large language models using Reinforcement Learning from Human Feedback, focusing on quality feedback and model performance.","featured":"2024-12-18","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":14,"scale":"shares"},{"title":"CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models","url":"/papers/arxiv/2412.12093/","summary":"Portrait Avatars: CAP4D is a method that uses a unique model to create and animate realistic 4D portrait avatars from any number of reference images in real time.","featured":"2024-12-18","label":"Machine learning","topic":"ML & AI Methods","cites":60,"score":228,"scale":"shares"},{"title":"Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos","url":"/papers/arxiv/2412.09621/","summary":"Learning Motion from Videos: The authors have developed a system that mines high-quality 4D reconstructions from internet videos, allowing for the prediction of structure and 3D motion from real-world image pairs.","featured":"2024-12-18","label":"Machine learning","topic":"ML & AI Methods","cites":94,"score":38,"scale":"shares"},{"title":"GenEx: Generating an Explorable World","url":"/papers/arxiv/2412.09624/","summary":"Explorable World: GenEx is a system that plans complex world exploration, guided by its generative imagination about the surrounding environments, enabling AI agents to perform complex tasks.","featured":"2024-12-18","label":"Machine learning","topic":"ML & AI Methods","cites":24,"score":25,"scale":"shares"},{"title":"Spectral Image Tokenizer","url":"/papers/arxiv/2412.09607/","summary":"The paper suggests a novel method for tokenizing images for autoregressive transformer-based image generation, using a discrete wavelet transform for a coarse-to-fine representation, offering benefits like improved next-token prediction and the ability to reconstruct varying resolution images.","featured":"2024-12-18","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":20,"scale":"shares"},{"title":"MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization","url":"/papers/arxiv/2412.12098/","summary":"The research introduces MaxInfoRL, a reinforcement learning framework that balances exploration by directing it towards informative transitions, demonstrating superior performance in challenging exploration problems and complex visual control tasks.","featured":"2024-12-18","label":"Machine learning","topic":"ML & AI Methods","cites":38,"score":14,"scale":"shares"},{"title":"Early Detection of Child Violence with ML","url":"/papers/repec/eee-cysrev-v-166-y-2024-i-c-s0190740924005048/","summary":"A paper uses machine learning to predict child abuse in Argentina, suggesting these models could help identify at-risk households early.","featured":"2024-12-18","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":10,"scale":"shares"},{"title":"Stock Price Reaction to Managerial Soft Info","url":"/papers/repec/taf-hbhfxx-v-25-y-2024-i-4-p-481-495/","summary":"A study uses unsupervised machine learning to analyze the effect of spontaneous information shared during conference calls on company stock prices.","featured":"2024-12-18","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":9,"scale":"shares"},{"title":"Charge Estimation Methods for Li-Ion Batteries","url":"/papers/ssrn/5045879/","summary":"The study evaluates different methods of estimating the State of Charge for Li-ion batteries in electric vehicles, including their error ratios and compatibility with machine learning models.","featured":"2024-12-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Smart Learning: AI Integration","url":"/papers/ssrn/5047034/","summary":"AI Integration: The report details the creation of an AI-based hybrid system for SMART learning, using machine learning to predict student course completion success based on performance data.","featured":"2024-12-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Reinforcement Learning for Rabi Model","url":"/papers/ssrn/5046703/","summary":"The paper introduces a control scheme using reinforcement learning to improve entanglement in a Rabi model, highlighting its resistance to dissipation and broad applicability.","featured":"2024-12-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"AI Algorithms & Machine Learning for Big Data","url":"/papers/ssrn/5048954/","summary":"AI and machine learning are revolutionizing big data processing by analyzing large data volumes and complex patterns, with a focus on automation, integration, and explainable AI.","featured":"2024-12-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Baijiu Aroma Analysis with OIRD & ML","url":"/papers/ssrn/5051151/","summary":"A new method using oblique-incidence reflectance difference technology and machine learning algorithms can detect Baijiu aroma and trace components faster and more sensitively than traditional methods.","featured":"2024-12-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optimal Scaling in Serverless Computing","url":"/papers/ssrn/5042760/","summary":"Serverless computing in cloud data centers can be optimized using machine learning and feature engineering techniques to predict the best times for provisioning Azure Function Apps.","featured":"2024-12-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms","url":"/papers/arxiv/2412.06193/","summary":"The research uses AI to study tail risk in US financial markets, revealing a significant spillover effect from the credit market to the stock market.","featured":"2024-12-12","label":"arXiv","topic":"ML & AI Methods","cites":33,"score":6,"scale":"shares"},{"title":"Enhancing Fourier pricing with machine learning","url":"/papers/arxiv/2412.05070/","summary":"The study suggests using machine learning to fine-tune Fourier methods for pricing European options, leading to quicker, error-controlled algorithms.","featured":"2024-12-12","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":5,"scale":"shares"},{"title":"Follow the money: A startup-based measure of AI exposure across occupations, industries, and regions","url":"/papers/arxiv/2412.04924/","summary":"The AI Startup Exposure index suggests that high-skilled jobs are not uniformly at high risk from AI, with AI adoption in workplaces being gradual and influenced by social factors and technical feasibility of AI applications.","featured":"2024-12-12","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":7,"scale":"shares"},{"title":"AI and the Law","url":"/papers/arxiv/2412.05090/","summary":"The article discusses the potential impact of generative AI on law, suggesting it may decrease the need for court services in property and contract law, but increase litigation in areas like tort law.","featured":"2024-12-12","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":7,"scale":"shares"},{"title":"A Machine Learning Algorithm for Finite-Horizon Stochastic Control Problems in Economics","url":"/papers/arxiv/2411.08668/","summary":"A proposed machine learning algorithm effectively solves complex, time-limited stochastic control problems, showing good convergence and efficiency without depending on the Bellman equation.","featured":"2024-12-12","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":12,"scale":"shares"},{"title":"Reinforcement Learning: An Overview","url":"/papers/arxiv/2412.05265/","summary":"The manuscript offers a detailed review of deep reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, and model-based methods.","featured":"2024-12-12","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":1040,"scale":"shares"},{"title":"[MASK] is All You Need","url":"/papers/arxiv/2412.06787/","summary":"The study suggests using discrete-state models to connect Masked Generative and Non-autoregressive Diffusion models, and to redefine tasks like image segmentation as an unmasking process.","featured":"2024-12-12","label":"Machine learning","topic":"ML & AI Methods","cites":10,"score":89,"scale":"shares"},{"title":"Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data","url":"/papers/arxiv/2412.07762/","summary":"The article introduces Warm-start RL (WSRL), a new reinforcement learning approach that doesn't require offline data, leading to quicker learning and better performance than previous algorithms.","featured":"2024-12-12","label":"Machine learning","topic":"ML & AI Methods","cites":70,"score":37,"scale":"shares"},{"title":"Birth and Death of a Rose","url":"/papers/arxiv/2412.05278/","summary":"The research presents a technique for creating temporal object intrinsics like a blooming rose from pre-existing 2D diffusion models, allowing for the depiction of dynamic objects from any angle and lighting.","featured":"2024-12-12","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":33,"scale":"shares"},{"title":"Chemist-aligned retrosynthesis by ensembling diverse inductive bias models.","url":"/papers/arxiv/2412.05269/","summary":"The paper suggests Chimera, a system for creating highly precise reaction models for chemical syntheses, which outperforms all major models by combining predictions from various sources using a learning-based ensembling strategy.","featured":"2024-12-12","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":20,"scale":"shares"},{"title":"Correlation Matrix Estimation with Reinforcement Learning","url":"/papers/repec/eee-finana-v-96-y-2024-i-pa-s1057521924005040/","summary":"The paper introduces a data-driven approach using reinforcement learning to improve the correlation and covariance matrix, demonstrating superior performance in volatility, Sharpe ratio, and downside risk.","featured":"2024-12-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Clustering in Financial Markets","url":"/papers/repec/ora-journl-v-33-y-2024-i-1-p-330-336/","summary":"The article reviews the use of machine learning clustering techniques in financial markets and stock investing, discussing their potential and limitations.","featured":"2024-12-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":27,"scale":"shares"},{"title":"Objectionable Web Content Filtering System","url":"/papers/repec/bjf-journl-v-9-y-2024-i-11-p-51-60/","summary":"The research is focused on developing a machine learning system to block inappropriate web content and alert parents when children encounter such content.","featured":"2024-12-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":24,"scale":"shares"},{"title":"Enhancing supply chain security with automated machine learning","url":"/papers/arxiv/2406.13166/","summary":"The paper introduces an automated machine learning framework to improve supply chain security by detecting fraud, predicting maintenance needs, and forecasting material backorders, thereby increasing accuracy rates and operational efficiency.","featured":"2024-12-04","label":"arXiv","topic":"ML & AI Methods","cites":11,"score":8,"scale":"shares"},{"title":"InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma","url":"/papers/arxiv/2411.09856/","summary":"InvestESG is a new benchmark using advanced learning to study the effects of ESG disclosure mandates on corporate climate investments, indicating that ESG-aware investors can boost corporate cooperation and mitigate climate risks.","featured":"2024-12-04","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":6,"scale":"shares"},{"title":"Another look at statistical inference with machine learning-imputed data","url":"/papers/arxiv/2411.19908/","summary":"The Chen and Chen estimator balances robustness and statistical efficiency in machine learning models, making it the top choice for prediction-based inference.","featured":"2024-12-04","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":8,"scale":"shares"},{"title":"Incremental Multi-Scene Modeling via Continual Neural Graphics Primitives","url":"/papers/arxiv/2411.19903/","summary":"Modeling Scenes: The C^3-NeRF framework can incorporate multiple 3D scenes into a single neural radiance field, showing the ability to adapt to new scenes without needing old data or extra parameters.","featured":"2024-12-04","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":8,"scale":"shares"},{"title":"Adaptive Informed Deep Neural Networks for Power Flow Analysis","url":"/papers/arxiv/2412.02659/","summary":"The study presents PINN4PF, a deep learning structure for power flow analysis that effectively captures the nonlinear dynamics of large-scale modern power systems, surpassing both linear regression models and black-box NN.","featured":"2024-12-04","label":"Machine learning","topic":"ML & AI Methods","cites":6,"score":4,"scale":"shares"},{"title":"On the consistency of hyper-parameter selection in value-based deep reinforcement learning","url":"/papers/arxiv/2406.17523/","summary":"The paper explores the reliability of hyper-parameter selection in value-based deep reinforcement learning agents, introducing a new score to measure the consistency and reliability of different hyper-parameters.","featured":"2024-12-04","label":"Machine learning","topic":"ML & AI Methods","cites":23,"score":83,"scale":"shares"},{"title":"Two Tales of Single-Phase Contrastive Hebbian Learning","url":"/papers/arxiv/2402.08573/","summary":"The authors lay the groundwork for the dual propagation method, a local learning algorithm for artificial neurons, and highlight its stability in relation to a specific adjoint state method, regardless of asymmetric nudging.","featured":"2024-12-04","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":67,"scale":"shares"},{"title":"AI Impact on Society","url":"/papers/repec/aes-dbjour-v-14-y-2023-i-1-p-61-75/","summary":"The paper focuses on the influence of AI, particularly the chatbot ChatGPT, on education and the job market, based on a survey of Romanian corporate employees.","featured":"2024-12-04","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":0,"scale":"shares"},{"title":"Deep Learning in Quantitative Economics","url":"/papers/ssrn/5030252/","summary":"Deep learning can effectively tackle the issue of dimensionality in quantitative economics, particularly in dynamic equilibrium models.","featured":"2024-11-27","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Dual Interpretation of ML Predictions","url":"/papers/ssrn/5029492/","summary":"The paper proposes that machine learning predictions can be interpreted as a linear combination of in-sample values of the predicted variable.","featured":"2024-11-27","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"HighPerformance Machine Learning in Fintech","url":"/papers/ssrn/5030509/","summary":"A high-performance computing engine designed for machine learning in FinTech has been presented, showing enhanced runtime performance and strategy evaluations.","featured":"2024-11-27","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Learning Securities Lending Dynamics","url":"/papers/ssrn/5029772/","summary":"A new model suggests that short sellers provide negative information to securities lenders, impacting institutional investors' decisions on lending, trading, and governance.","featured":"2024-11-27","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Model-Based Reinforcement Learning in Diffusion Environments","url":"/papers/ssrn/5030271/","summary":"A new framework for continuous-time model-based reinforcement learning, which reduces the discrepancy between the model-based value function and empirical rewards, could improve upon traditional statistical methods.","featured":"2024-11-27","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"The Illusion of Collusion","url":"/papers/arxiv/2411.16574/","summary":"Research shows that machine learning algorithms can unknowingly learn collusive behavior in competitive scenarios, posing a challenge for regulators to prevent algorithmic collusion.","featured":"2024-11-27","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":4,"scale":"shares"},{"title":"OminiControl: Minimal and Universal Control for Diffusion Transformer","url":"/papers/arxiv/2411.15098/","summary":"OminiControl, a new framework that incorporates image conditions into pre-trained Diffusion Transformer models, is introduced, surpassing existing models in conditional generation tasks.","featured":"2024-11-27","label":"Machine learning","topic":"ML & AI Methods","cites":370,"score":264,"scale":"shares"},{"title":"Learning Humanoid Locomotion with Perceptive Internal Model","url":"/papers/arxiv/2411.14386/","summary":"The article introduces the Perceptive Internal Model (PIM), a method that uses elevation maps for stable humanoid robot movement across different terrains and sensor setups.","featured":"2024-11-27","label":"Machine learning","topic":"ML & AI Methods","cites":115,"score":86,"scale":"shares"},{"title":"Whack-a-Chip: The Futility of Hardware-Centric Export Controls","url":"/papers/arxiv/2411.14425/","summary":"The study reveals how Chinese companies like Tencent are bypassing U.S. export controls to use semiconductors in advanced AI models.","featured":"2024-11-27","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":33,"scale":"shares"},{"title":"Stable Flow: Vital Layers for Training-Free Image Editing","url":"/papers/arxiv/2411.14430/","summary":"The research introduces a method to identify crucial layers in Diffusion Transformer models, improving image editing and inversion methods.","featured":"2024-11-27","label":"Machine learning","topic":"ML & AI Methods","cites":124,"score":21,"scale":"shares"},{"title":"Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks","url":"/papers/arxiv/2305.01626/","summary":"The paper discusses spontaneous concatenation in convolutional neural networks trained on acoustic recordings, offering a potential neural method for modeling syntax from raw acoustic inputs.","featured":"2024-11-27","label":"Machine learning","topic":"ML & AI Methods","cites":6,"score":53,"scale":"shares"},{"title":"Data and Creativity in Marketing","url":"/papers/repec/zbw-hsgmrs-306250/","summary":"The article discusses the profound influence of artificial intelligence on marketing, highlighting the importance of combining data and creativity for the future of the industry.","featured":"2024-11-27","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"NeuralDEM - Real-time Simulation of Industrial Particulate Flows","url":"/papers/arxiv/2411.09678/","summary":"Particulate Flow Simulation: NeuralDEM is a deep learning approach that replaces slow routines in the discrete element method (DEM), allowing for quicker and more efficient simulations of large fluid-mechanical and particulate systems.","featured":"2024-11-20","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":230,"scale":"shares"},{"title":"How Do Machine Learning Models Change?","url":"/papers/arxiv/2411.09645/","summary":"A large-scale study of Hugging Face models reveals patterns in commit and release activities, highlighting the continuous improvement in machine learning models.","featured":"2024-11-20","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":12,"scale":"shares"},{"title":"Towards a Classification of Open-Source ML Models and Datasets for Software Engineering","url":"/papers/arxiv/2411.09683/","summary":"A study classifies Pre-Trained Models and datasets on Hugging Face using a Software Engineering approach, indicating a need for more task coverage to better integrate machine learning in software engineering.","featured":"2024-11-20","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":11,"scale":"shares"},{"title":"Image Matching Filtering and Refinement by Planes and Beyond","url":"/papers/arxiv/2411.09484/","summary":"A new non-deep learning method for image matching is introduced, showing superior or equivalent performance to recent state-of-the-art deep learning methods.","featured":"2024-11-20","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":10,"scale":"shares"},{"title":"On the Foundation Model for Cardiac MRI Reconstruction","url":"/papers/arxiv/2411.10403/","summary":"A new foundation model for cardiac magnetic resonance imaging is proposed, which improves image quality across various protocols and outperforms traditional machine learning methods.","featured":"2024-11-20","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":10,"scale":"shares"},{"title":"Learning Diffusion Priors from Observations by Expectation Maximization","url":"/papers/arxiv/2405.13712/","summary":"A new method using the expectation-maximization algorithm has been developed to train diffusion models from incomplete and noisy data, enhancing their effectiveness for subsequent tasks.","featured":"2024-11-20","label":"Machine learning","topic":"ML & AI Methods","cites":75,"score":94,"scale":"shares"},{"title":"Data Prep for ML and AI","url":"/papers/repec/nwe-iitfed-y-2024-i-1-p-146-153/","summary":"The similarities between data preparation for machine learning and data warehouses can help automate the data preparation process.","featured":"2024-11-20","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":21,"scale":"shares"},{"title":"Transfer Function Prediction with Autoencoders","url":"/papers/ssrn/5013124/","summary":"A deep learning framework has been developed to predict transfer functions in structure-acoustic models, reducing computational time compared to traditional methods.","featured":"2024-11-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Generalizability of Surrogate Models","url":"/papers/ssrn/5013717/","summary":"A study highlights the potential of deep learning models to extract input data from output data in building energy modelling.","featured":"2024-11-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Magnetic Field Effects on Crystal Growth","url":"/papers/ssrn/5012480/","summary":"A study using simulations and machine learning found that a traveling magnetic field significantly alters the growth of βGa2O3 crystals.","featured":"2024-11-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Memory and Beliefs in Markets","url":"/papers/ssrn/5013622/","summary":"Sell-side stock analysts' belief formation is influenced by memory distortions, with analysts often over-recalling distant historical episodes and under-recalling them during crises, according to a machine learning model.","featured":"2024-11-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research","url":"/papers/arxiv/2411.04788/","summary":"The study suggests a multi-agent system for financial investment research that performs better than traditional models by adapting to market conditions and optimizing performance. This shows the potential of multi-agent systems in improving financial analysis and investment decisions.","featured":"2024-11-13","label":"arXiv","topic":"ML & AI Methods","cites":42,"score":3,"scale":"shares"},{"title":"Age-Normalized Testosterone Peaks at Series B for Male Startup Founders","url":"/papers/arxiv/2411.03361/","summary":"A study of 107 male Y Combinator founders shows a link between testosterone levels and company stage, with testosterone rising by 55.7% from pre-seed to seed funding, peaking at Series B, then dropping by 42.2% after Series B, indicating early startup success boosts confidence, while later-stage pressures increase stress.","featured":"2024-11-13","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":42,"scale":"shares"},{"title":"Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models","url":"/papers/arxiv/2411.04996/","summary":"The Mixture-of-Transformers (MoT) is a sparse multi-modal transformer architecture that reduces pretraining costs and allows modality-specific processing with global self-attention.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":161,"score":58,"scale":"shares"},{"title":"Non-equilibrium active noise enhances generative memory in diffusion models","url":"/papers/arxiv/2411.07233/","summary":"The generative performance of diffusion models can be enhanced by using noise sources with temporal correlations for data destruction in the forward process.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":25,"scale":"shares"},{"title":"ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning","url":"/papers/arxiv/2411.05003/","summary":"ReCapture is a method for creating new videos with unique camera trajectories from a single video, allowing for the regeneration of the video from different angles and cinematic camera motion.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":76,"score":23,"scale":"shares"},{"title":"Watermark Anything with Localized Messages","url":"/papers/arxiv/2411.07231/","summary":"The Watermark Anything Model (WAM) is a deep-learning model that can embed and extract hidden watermarks in specific areas of an image.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":72,"score":17,"scale":"shares"},{"title":"Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models","url":"/papers/arxiv/2411.07232/","summary":"Object Insertion: Add-it is a training-free approach for semantic image editing that uses diffusion models to add objects into images based on text instructions, ensuring natural placement and detail preservation.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":48,"score":11,"scale":"shares"},{"title":"Nteasee: Understanding Needs in AI for Health in Africa - A Mixed-Methods Study of Expert and General Population Perspectives","url":"/papers/arxiv/2409.12197/","summary":"Research highlights the potential of AI in African healthcare, but emphasizes the need for culturally sensitive approaches and addresses concerns about trust, ethics, and systemic barriers.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":4,"score":77,"scale":"shares"},{"title":"Optimization without Retraction on the Random Generalized Stiefel Manifold","url":"/papers/arxiv/2405.01702/","summary":"A new method for optimization over matrices is proposed, which is cost-effective and efficient in various machine learning applications.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":57,"scale":"shares"},{"title":"Meta-models for transfer learning in source localization","url":"/papers/arxiv/2305.08657/","summary":"A Bayesian multilevel approach is used to predict model hyperparameters in acoustic emission experiments, demonstrating its use in source localization.","featured":"2024-11-13","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":40,"scale":"shares"},{"title":"Index Tracking with Shapley Explanations","url":"/papers/repec/eee-finana-v-95-y-2024-i-pc-s1057521924004198/","summary":"The paper suggests using a one-dimensional Pointwise Convolutional Autoencoder and Shapley Additive Explanations for index tracking, outperforming other stock selection strategies in various financial markets.","featured":"2024-11-13","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Human Capital and AI Adoption","url":"/papers/ssrn/5010602/","summary":"A study suggests that increasing the number of ICT engineers in firms not using AI could boost their likelihood to adopt AI, but significant investment is needed to bridge the current ICT skills gap.","featured":"2024-11-06","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Regulating Medical AI","url":"/papers/ssrn/5009572/","summary":"The FDA is considering Predetermined Change Control Plans (PCCP) to ease the administrative burden of AI algorithms learning from new data, but its effect on Good Machine Learning Practices (GMLP) compliance is unclear.","featured":"2024-11-06","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Machine Learning in Catalysis","url":"/papers/ssrn/5010723/","summary":"Data-driven modelling, especially through machine learning, is a promising alternative in catalysis due to the limitations of traditional models.","featured":"2024-11-06","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning in Business Research","url":"/papers/ssrn/4981802/","summary":"The study reveals a lack of transparency in predictive machine learning studies in top business and economic journals, leading to fewer citations due to inadequate benchmarking against traditional statistical models.","featured":"2024-11-06","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Generative AI and Security Operations Center Productivity: Evidence from Live Operations","url":"/papers/arxiv/2411.03116/","summary":"The study explores the effect of generative AI tools on security operations center productivity, noting a 30.13% decrease in security incident resolution time.","featured":"2024-11-06","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":2,"scale":"shares"},{"title":"EgoMimic: Scaling Imitation Learning via Egocentric Video","url":"/papers/arxiv/2410.24221/","summary":"EgoMimic, a new framework, improves manipulation tasks performance using human embodiment data, proving more effective than existing imitation learning methods and showing that human data is more valuable than robot data.","featured":"2024-11-06","label":"Machine learning","topic":"ML & AI Methods","cites":245,"score":13,"scale":"shares"},{"title":"Towards Generative Ray Path Sampling for Faster Point-to-Point Ray Tracing","url":"/papers/arxiv/2410.23773/","summary":"The article introduces a Machine Learning-enhanced Ray Tracing method for efficient radio propagation modeling, reducing computational effort while maintaining accuracy.","featured":"2024-11-06","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":11,"scale":"shares"},{"title":"Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images","url":"/papers/arxiv/2411.02354/","summary":"The paper explores the use of machine learning to analyze Maternal Inflammatory Response (MIR) from whole slide images, achieving up to 88.5% balanced accuracy.","featured":"2024-11-06","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":10,"scale":"shares"},{"title":"Oblivious Defense in ML Models: Backdoor Removal without Detection","url":"/papers/arxiv/2411.03279/","summary":"The research proposes strategies to defend against undetectable backdoors in machine learning models, using random self-reducibility-inspired techniques.","featured":"2024-11-06","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":10,"scale":"shares"},{"title":"On the Benefits of Active Data Collection in Operator Learning","url":"/papers/arxiv/2410.19725/","summary":"The study shows that active data collection methods are more effective than passive ones in operator learning, especially when dealing with linear operators and input functions from a mean-zero stochastic process.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":11,"scale":"shares"},{"title":"Multi-modal AI for comprehensive breast cancer prognostication","url":"/papers/arxiv/2410.21256/","summary":"A new AI test for breast cancer patient stratification, combining digital pathology and clinical characteristics, has been developed, showing higher accuracy than the current standard and applicability across all major breast cancer subtypes.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":9,"scale":"shares"},{"title":"FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning","url":"/papers/doi/10-1145-3677052-3698597/","summary":"Financial Intelligence: The paper introduces FISHNET, a new system for generating financial intelligence from large data sources, offering scalability, flexibility, and data integrity.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":7,"scale":"shares"},{"title":"Arabic Music Classification and Generation using Deep Learning","url":"/papers/arxiv/2410.19719/","summary":"The study suggests a machine learning method using a convolutional neural network for classifying and creating new and traditional Egyptian music by composer, with 81.4% accuracy.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":7,"scale":"shares"},{"title":"Modular Duality in Deep Learning","url":"/papers/arxiv/2410.21265/","summary":"The article presents a new theory of modular dualization for general neural networks, providing a theoretical basis for fast and scalable training algorithms, potentially leading to a new generation of optimizers for neural architectures.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":75,"score":6,"scale":"shares"},{"title":"Empirical Design in Reinforcement Learning","url":"/papers/arxiv/2304.01315/","summary":"The article highlights the importance of proper statistical evidence and avoiding common errors in empirical design for effective reinforcement learning experiments.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":77,"score":299,"scale":"shares"},{"title":"Unbounded: A Generative Infinite Game of Character Life Simulation","url":"/papers/arxiv/2410.18975/","summary":"A Generative Game: The paper presents Unbounded, a generative infinite game using a large language model and a dynamic image prompt Adapter for real-time game creation.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":20,"score":74,"scale":"shares"},{"title":"Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks","url":"/papers/arxiv/2309.12927/","summary":"The research shows that recurrent neural networks improve performance and generalization by adapting timescales for memory-dependent tasks.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":63,"scale":"shares"},{"title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","url":"/papers/arxiv/2406.19380/","summary":"The article presents TabReD, a collection of industry-grade tabular datasets, showing that simple MLP-like architectures and GBDT perform best in real-world conditions.","featured":"2024-10-31","label":"Machine learning","topic":"ML & AI Methods","cites":36,"score":49,"scale":"shares"},{"title":"AI for Innovation Analysis","url":"/papers/repec/wsi-ijimxx-v-28-y-2024-i-05n06-n-s1363919624500208/","summary":"The study offers a detailed analysis of AI, machine learning, and big data's role in fostering innovation, identifying key research themes and trends from 1991 to 2021.","featured":"2024-10-31","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Machine Learning for Engineering Constants Prediction","url":"/papers/ssrn/4991455/","summary":"The study uses machine learning and the finite element method to determine the mechanical properties of certain composites, offering a more efficient alternative to traditional methods.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"IPO Pricing Prediction with Lasso-Neural Networks","url":"/papers/ssrn/4990940/","summary":"A new model using Lasso neural networks has been developed to predict IPO pricing for Chinese companies, with retained earnings per share being a key factor.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Fedmse: Semi-Supervised Federated Learning for IoT Detection","url":"/papers/ssrn/4990105/","summary":"Semi-Supervised Federated Learning for IoT Detection: A new federated learning approach has been proposed to enhance IoT network intrusion detection, integrating the Shrink Autoencoder and Centroid one-class classifier with a new aggregation algorithm.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Composite Laminate Design","url":"/papers/ssrn/4991453/","summary":"The paper discusses a machine learning method for designing composite laminates efficiently, using a generator and discriminator to predict mechanical properties with limited data.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Generative AI Training","url":"/papers/ssrn/4993782/","summary":"The article suggests that using copyrighted data to train generative AI models without licenses is a copyright infringement, as the DSM Directive's exceptions for text and data mining do not apply.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Technology Value Prediction","url":"/papers/ssrn/4992440/","summary":"The study introduces a deep-learning model that predicts the economic value of technology using patent and firm data, showing better prediction performance than other models.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Reinforcement Learning in Market-Making","url":"/papers/ssrn/4991392/","summary":"The paper presents a deep reinforcement learning framework for optimal market-making trading, using the Soft Actor-Critic algorithm to manage complex, high-dimensional problems with continuous state and action spaces.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"AI in Working Capital Management","url":"/papers/ssrn/4992621/","summary":"AI enhances working capital management in the auto industry by improving demand forecasting, streamlining accounts, managing inventory, and spotting financial anomalies.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AIPowered Data Warehouse Solutions","url":"/papers/ssrn/4993596/","summary":"AI integration into data warehousing boosts data processing efficiency, accuracy, and scalability, enabling automated data extraction, real-time analytics, and improved data quality.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Telecom Network Spectrum Optimization","url":"/papers/ssrn/4989103/","summary":"The research investigates the application of Artificial Intelligence and Machine Learning, particularly Artificial Neural Networks, in telecommunications for optimizing spectrum management and addressing industry issues like predictive maintenance, virtual assistance, network optimization, fraud prevention, and revenue growth.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optimizing Predictive Maintenance with Machine Learning And Iot: A Business Strategy For Reducing Downtime And Operational Costs","url":"/papers/ssrn/4994457/","summary":"The study presents a predictive maintenance framework that employs IoT sensors and advanced Machine Learning algorithms to anticipate equipment failures and carry out proactive maintenance, leading to a 30-40% decrease in unexpected downtime and 20-30% in maintenance costs.","featured":"2024-10-23","label":"SSRN","topic":"ML & AI Methods","cites":7,"score":3,"scale":"shares"},{"title":"Scalable Signature-Based Distribution Regression via Reference Sets","url":"/papers/arxiv/2410.09196/","summary":"The paper introduces a new methodology for Distribution Regression on stochastic processes, resolving estimation uncertainties and expanding its use in various learning tasks across different fields.","featured":"2024-10-23","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":3,"scale":"shares"},{"title":"Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens","url":"/papers/arxiv/2410.13863/","summary":"The study explores text-to-image generation, finding continuous token-based models offer superior visual quality and random-order models score higher on the GenEval benchmark, leading to a new model, Fluid.","featured":"2024-10-23","label":"Machine learning","topic":"ML & AI Methods","cites":157,"score":194,"scale":"shares"},{"title":"The Disparate Benefits of Deep Ensembles","url":"/papers/arxiv/2410.13831/","summary":"Deep Ensembles, a type of AI, can unintentionally favor certain groups, leading to unfair benefits; this can be reduced through post-processing without affecting performance.","featured":"2024-10-23","label":"Machine learning","topic":"ML & AI Methods","cites":4,"score":15,"scale":"shares"},{"title":"Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making","url":"/papers/arxiv/2410.14468/","summary":"The Simple to Complex Collaborative Decision framework uses reinforcement learning to enhance safety and efficiency in autonomous vehicle decision-making, guided by a teacher model to avoid danger.","featured":"2024-10-23","label":"Machine learning","topic":"ML & AI Methods","cites":26,"score":10,"scale":"shares"},{"title":"Agent-to-Sim: Learning Interactive Behavior Models from Casual Longitudinal Videos","url":"/papers/arxiv/2410.16259/","summary":"Agent-to-Sim (ATS) is a system that learns interactive behavior models of 3D agents from video collections, allowing transfer from real-life videos to a behavior simulator.","featured":"2024-10-23","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":10,"scale":"shares"},{"title":"Microsoft Copilot and Finance Workforce","url":"/papers/repec/bhx-ojtijf-v-9-y-2024-i-3-p-32-41-id-1918/","summary":"The paper explores the potential impact of the AI tool, Microsoft Copilot, on the finance workforce, suggesting a future balance between automation, skill evolution, and ethical considerations.","featured":"2024-10-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":10,"scale":"shares"},{"title":"Machine Learning for Gas-Liquid Flow","url":"/papers/ssrn/4988520/","summary":"The article assesses various machine learning frameworks for multiphase flow in oil and gas production to enhance prediction accuracy.","featured":"2024-10-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"ML Prediction of Plasma-Catalytic DRM","url":"/papers/ssrn/4986711/","summary":"A new machine learning model is created to predict and improve the plasmacatalytic dry reformation of methane over NiAl2O3 catalysts in a specific reactor.","featured":"2024-10-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Seismic White Noise Reduction","url":"/papers/ssrn/4984294/","summary":"The study shows that deep learning algorithms can effectively eliminate noise from seismic data, especially when the algorithm is trained to learn the signal instead of the noise.","featured":"2024-10-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"ML Integration in Financial Data","url":"/papers/ssrn/4984392/","summary":"Combining machine learning with financial data lakes improves predictive analytics, aiding in accurate predictions, fraud detection, and operation optimization.","featured":"2024-10-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Tracking Cement Activity via Satellites","url":"/papers/ssrn/4986361/","summary":"The study uses infrared satellite imagery and machine learning to monitor real-time economic activity in the global cement industry.","featured":"2024-10-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Evolution of AI and ML with Big Data","url":"/papers/ssrn/4988863/","summary":"The paper highlights the rapid advancement of AI and Machine Learning due to large datasets, leading to increased accuracy, efficiency, and versatility.","featured":"2024-10-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Depth Any Video with Scalable Synthetic Data","url":"/papers/arxiv/2410.10815/","summary":"The article presents Depth Any Video, a new model that uses synthetic data and video diffusion models to estimate video depth more accurately and consistently than previous models.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":66,"score":74,"scale":"shares"},{"title":"Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning","url":"/papers/arxiv/2410.10801/","summary":"The research investigates model merging in a multilingual context for Large Language Models, finding that objective-based and language-based merging methods enhance performance and safety.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":11,"scale":"shares"},{"title":"Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds","url":"/papers/arxiv/2410.12779/","summary":"The Geometry-Aware Generative Autoencoder (GAGA) addresses challenges of high-dimensional datasets by combining manifold learning with generative modeling.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":23,"score":8,"scale":"shares"},{"title":"Improving Long-Text Alignment for Text-to-Image Diffusion Models","url":"/papers/arxiv/2410.11817/","summary":"LongAlign, a new method for processing long texts, improves alignment in text-to-image diffusion models, overcoming limitations of existing encoding methods.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":7,"scale":"shares"},{"title":"Scaling Laws For Diffusion Transformers","url":"/papers/arxiv/2410.08184/","summary":"Experiments have confirmed the existence of scaling laws in Diffusion Transformers, aiding in determining optimal model size, data needs, and predicting text-to-image generation loss.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":42,"score":155,"scale":"shares"},{"title":"Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators","url":"/papers/arxiv/2402.13984/","summary":"The Stability-Aware Boltzmann Estimator Training has been introduced to improve the stability, data efficiency, and agreement with reference observables in Machine Learning Force Fields used in molecular dynamics simulations.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":17,"score":96,"scale":"shares"},{"title":"An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem","url":"/papers/arxiv/2404.17563/","summary":"A new framework represents each new ability in deep learning models as a basis function, providing analytic expressions for the emergence of new skills and scaling laws of the loss with various factors.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":19,"score":49,"scale":"shares"},{"title":"Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency","url":"/papers/arxiv/2410.06025/","summary":"The paper introduces SPELL, a method that enhances the diversity of text-to-image diffusion models while avoiding protected images, proving its superiority over other diversity methods.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":25,"score":28,"scale":"shares"},{"title":"cedar: Optimized and Unified Machine Learning Input Data Pipelines","url":"/papers/arxiv/2401.08895/","summary":"The study introduces cedar, a new programming framework for machine learning input data pipelines that enhances performance by applying a mix of optimizations, outperforming other systems.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":17,"score":23,"scale":"shares"},{"title":"Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning","url":"/papers/arxiv/2406.16257/","summary":"The article presents Sequence-aware Sharded Sliced Training (S3T), a new framework for machine unlearning that improves system deletion capabilities with minimal impact on model performance, proving more effective than other methods.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":37,"score":16,"scale":"shares"},{"title":"Learning Quadruped Locomotion Using Differentiable Simulation","url":"/papers/arxiv/2403.14864/","summary":"The paper proposes a new differentiable simulation framework for learning quadruped locomotion, showing its efficiency and effectiveness compared to traditional reinforcement learning methods.","featured":"2024-10-17","label":"Machine learning","topic":"ML & AI Methods","cites":49,"score":15,"scale":"shares"},{"title":"Importance of Hyperparameters in ML","url":"/papers/repec/cup-pscirm-v-12-y-2024-i-4-p-841-848-9/","summary":"A study reveals that only 20.31% of machine learning papers in political science journals report their hyperparameters and tuning methods, indicating a need for more transparency and robustness in machine learning models.","featured":"2024-10-17","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":21,"scale":"shares"},{"title":"Regulatory Intensity","url":"/papers/repec/oup-rfinst-v-36-y-2023-i-8-p-3311-3347/","summary":"Research using administrative data and machine-learning models shows that increased regulatory intensity raises costs and discourages companies from investing and hiring, especially financially constrained firms.","featured":"2024-10-17","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Partisanship in Financial Regulators","url":"/papers/repec/oup-rfinst-v-36-y-2023-i-11-p-4373-4416/","summary":"Machine learning analysis of language used in Congress and new SEC rules shows a significant increase in partisanship among SEC Commissioners from 2010-2019, while the Federal Reserve Board remains relatively nonpartisan.","featured":"2024-10-17","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Geoffrey Hinton's Neural Networks Contributions","url":"/papers/ssrn/4980068/","summary":"Geoffrey Hinton's contributions in neural networks, deep learning, and Capsule Networks have significantly influenced modern AI systems used in various fields like image and speech recognition, and natural language processing.","featured":"2024-10-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":224,"scale":"shares"},{"title":"MLBased CT Saturation Detection","url":"/papers/ssrn/4979169/","summary":"The study introduces a machine learning method for detecting saturation in Current Transformers using artificial neural networks and long short-term memory networks, enhancing anomaly detection in complex datasets.","featured":"2024-10-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Robust Decision-Making in ML","url":"/papers/ssrn/4979319/","summary":"The paper proposes the fusion of machine learning and optimization theory, suggesting algorithms that ensure balance and stability in intricate decision-making systems.","featured":"2024-10-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning for Stock Market Prediction","url":"/papers/ssrn/4975786/","summary":"The research applies machine learning models to predict future stock market indices, using data from the Nifty50 index and evaluating the models' effectiveness.","featured":"2024-10-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Cricket Team Maker","url":"/papers/ssrn/4977691/","summary":"A fantasy cricket team generator tool has been created, using advanced algorithms and machine learning to evaluate players and create optimal teams.","featured":"2024-10-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI in Accounting Systems","url":"/papers/ssrn/4977498/","summary":"A new course has been developed to introduce artificial intelligence into the accounting information systems curriculum, using various teaching methods to aid understanding.","featured":"2024-10-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Generative AI, Managerial Expectations, and Economic Activity","url":"/papers/arxiv/2410.03897/","summary":"Predicting Economic Indicators from Corporate Calls: The article discusses the use of Generative AI in analyzing over 120,000 corporate conference call transcripts to predict future economic indicators like GDP growth, production, and employment. This technology provides valuable insights for macroeconomic and microeconomic decision-making.","featured":"2024-10-09","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":2,"scale":"shares"},{"title":"Differential Transformer","url":"/papers/arxiv/2410.05258/","summary":"The Diff Transformer is a new language model architecture that enhances attention to relevant context and reduces noise, outperforming the standard Transformer in long-context modeling and key information retrieval.","featured":"2024-10-09","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":102,"scale":"shares"},{"title":"MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions","url":"/papers/arxiv/2410.02743/","summary":"The MA-RLHF framework integrates macro actions into the learning process of large language models, enhancing learning efficiency and performance in tasks like text summarization and dialogue generation.","featured":"2024-10-09","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":10,"scale":"shares"},{"title":"Packing's Impact on Malware Detection","url":"/papers/ssrn/4973174/","summary":"Research highlights the challenge of packing on static machine learning-based malware detection systems, posing a significant problem for static analysis and signature-based malware detection methods.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"PCM Properties Decision Support System","url":"/papers/ssrn/4972052/","summary":"A study successfully used machine learning algorithms to predict the mechanical properties of polymer composite materials, with the lasso regression algorithm showing the highest accuracy.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Automating Clash Resolution","url":"/papers/ssrn/4973972/","summary":"The use of supervised and reinforcement learning to automate clash resolution in software like Navisworks is being studied, but data availability limits effectiveness.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for SF6 Detection","url":"/papers/ssrn/4972283/","summary":"Machine learning is being used to predict the decomposition of SF6, a power grid insulating gas, using data from 52 publications.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optimizing Flue Gas Temperature","url":"/papers/ssrn/4973396/","summary":"A machine learning model accurately predicts and optimizes flue gas temperature in the sintering process, identifying key impacting parameters.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Revolutionizing the Machine Learning Lifecycle With Cutting-Edge Data Engineering","url":"/papers/ssrn/4972889/","summary":"The research explores the application of advanced data engineering methods to enhance the Machine Learning process, tackling issues from data analysis to implementation.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Enhancing Graph Network Models","url":"/papers/ssrn/4973874/","summary":"The study investigates the use of metaheuristics such as Simulated Annealing, Tabu Search, and Variable Neighborhood Search to improve the efficiency of graph network models like Graph Neural Networks and Graph Convolutional Networks.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for Speech Recognition","url":"/papers/ssrn/4972185/","summary":"The article emphasizes the role of voice classification in AI and machine learning for improved speech recognition, beneficial in areas like language identification, voice biometrics, speaker recognition, and general speech recognition.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Risks of Institutional Black Boxes","url":"/papers/ssrn/4971723/","summary":"The article explores the 'black boxes' in machine learning systems, differentiating between 'algorithmic black boxes' that are complex for human understanding, and 'institutional black boxes' that are confidential due to business or economic reasons, advocating for more transparency in machine learning usage in public institutions.","featured":"2024-10-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Can AI Enhance its Creativity to Beat Humans ?","url":"/papers/arxiv/2409.18776/","summary":"Who Wins?: Research indicates that AI often surpasses humans in creative tasks, but the results depend on the task and creativity criteria, highlighting the importance of human feedback.","featured":"2024-10-03","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":5,"scale":"shares"},{"title":"Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback","url":"/papers/arxiv/2409.18660/","summary":"A study on an online chess platform reveals that learning from AI feedback can lead to a loss of intellectual diversity and a widening skill gap, as higher-skilled individuals benefit more.","featured":"2024-10-03","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":2,"scale":"shares"},{"title":"Unconditional stability of a recurrent neural circuit implementing divisive normalization","url":"/papers/arxiv/2409.18946/","summary":"The research introduces ORGaNICs, a recurrent cortical circuit model that outperforms other models in image classification tasks and matches LSTMs in sequential tasks, offering dynamic divisive normalization and unconditional local stability.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":7,"scale":"shares"},{"title":"Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers","url":"/papers/arxiv/2409.20537/","summary":"The paper presents Heterogeneous Pre-trained Transformers (HPT), a method for training robotic models across various tasks, improving the performance of fine-tuned policies by over 20% on unseen tasks in both simulated and real-world environments.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":200,"score":5,"scale":"shares"},{"title":"Maia-2: A Unified Model for Human-AI Alignment in Chess","url":"/papers/arxiv/2409.20553/","summary":"Researchers have proposed a unified model for aligning human and AI strategies in chess, which could lead to AI-based teaching tools.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":37,"score":4,"scale":"shares"},{"title":"On Rademacher Complexity-based Generalization Bounds for Deep Learning","url":"/papers/arxiv/2208.04284/","summary":"A Rademacher complexity-based method provides reliable generalisation bounds on Convolutional Neural Networks (CNNs) for image classification, with complexity independent of network length for certain activation functions.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":25,"score":34,"scale":"shares"},{"title":"FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner","url":"/papers/arxiv/2409.18128/","summary":"Real-time Image Generation: FlowTurbo is introduced, a framework that accelerates the sampling of flow-based generative models for faster image generation, setting a new field standard.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":5,"score":33,"scale":"shares"},{"title":"FiT: Flexible Vision Transformer for Diffusion Model","url":"/papers/arxiv/2402.12376/","summary":"Improved Vision Transformer: The Flexible Vision Transformer (FiTv2) is presented, a design that generates images with unrestricted resolutions and aspect ratios, showing excellent performance across various resolutions.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":95,"score":28,"scale":"shares"},{"title":"Does Vision Accelerate Hierarchical Generalization in Neural Language Learners?","url":"/papers/arxiv/2302.00667/","summary":"The study investigates the influence of visual information on syntactic generalization in language models, revealing that strong alignments between linguistic and visual elements can improve syntactic generalization.","featured":"2024-10-03","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":24,"scale":"shares"},{"title":"AI and Big Data Token Herding","url":"/papers/repec/eee-riibaf-v-72-y-2024-i-pa-s027553192400299x/","summary":"Research indicates that investors tend to follow the crowd in AI and big data token markets, especially during crises, highlighting the need for regulatory intervention for market stability.","featured":"2024-10-03","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"A Copula-Guided In-Model Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images","url":"/papers/arxiv/2303.17448/","summary":"NN-Copula-CD, a new method for detecting changes in heterogeneous remote sensing images, combines copula theory and deep neural networks, improving disaster monitoring and land-use management.","featured":"2024-09-25","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":23,"scale":"shares"},{"title":"Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data","url":"/papers/arxiv/2312.16810/","summary":"A review of machine learning methods for diagnosing and predicting industrial system issues using open-source datasets offers a unified framework and highlights future research directions in the Prognostics and Health Management field.","featured":"2024-09-25","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":20,"scale":"shares"},{"title":"Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning","url":"/papers/arxiv/2409.12045/","summary":"The paper extends the safe exploration method, ATACOM, with learnable constraints for reinforcement learning in real-world robots, emphasizing long-term safety and uncertainty management.","featured":"2024-09-25","label":"Machine learning","topic":"ML & AI Methods","cites":5,"score":10,"scale":"shares"},{"title":"Machine Learning in Cybersecurity","url":"/papers/ssrn/4955692/","summary":"The study suggests a novel method to optimize machine learning models by identifying the most effective data preprocessing strategy, based on the correlation between datasets' metafeatures and performance response variables.","featured":"2024-09-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning in Propane Flow","url":"/papers/ssrn/4954901/","summary":"The research introduces a one-dimensional simulation method for quick and precise calculation of heat transfer parameters of propane condensing flow in the minichannel of a Liquefied Natural Gas vaporizer.","featured":"2024-09-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning","url":"/papers/arxiv/2409.08980/","summary":"Young Adult Factors: Machine learning research identifies risk and benefit perceptions, attitudes toward feasibility and usability, and prior experience as key factors influencing young adults' trust in Autonomous Vehicles.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":11,"score":7,"scale":"shares"},{"title":"Model-independent variable selection via the rule-based variable priority","url":"/papers/arxiv/2409.09003/","summary":"The paper presents Variable Priority (VarPro), a new model-independent method for variable selection in machine learning, which doesn't need artificial data or prediction error evaluation and consistently filters noise variables.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":4,"score":7,"scale":"shares"},{"title":"VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization","url":"/papers/arxiv/2409.09011/","summary":"The article introduces VAE Explainer, a browser-based interactive tool that simplifies understanding of machine learning concepts through interactive model inputs and outputs.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":6,"scale":"shares"},{"title":"Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation","url":"/papers/arxiv/2409.09016/","summary":"Robotic Manipulation Control: The article presents CLOVER, a robotics control framework that uses feedback mechanisms for improved adaptive control, demonstrating superior performance in real-world tasks and on the CALVIN benchmark.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":76,"score":6,"scale":"shares"},{"title":"PINNfluence: Interpreting PINNs through Influence Functions","url":"/papers/arxiv/2409.08958/","summary":"The article discusses the use of influence functions to validate and debug physics-informed neural networks, suggesting potential for further research in this area.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":5,"scale":"shares"},{"title":"Biomimetic Frontend for Differentiable Audio Processing","url":"/papers/arxiv/2409.08997/","summary":"The authors propose a model of human hearing that combines traditional signal processing with deep-learning, resulting in an efficient and explainable model that performs well in audio processing tasks with limited training data.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":5,"scale":"shares"},{"title":"NPGA: Neural Parametric Gaussian Avatars","url":"/papers/arxiv/2405.19331/","summary":"Scientists have created Neural Parametric Gaussian Avatars (NPGA), a new method for making high-quality, controllable avatars from multi-view videos, surpassing previous avatar technologies in self-reenactment tasks.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":61,"score":281,"scale":"shares"},{"title":"Boundary Attention: Learning Curves, Corners, Junctions and Grouping","url":"/papers/arxiv/2401.00935/","summary":"A new lightweight network has been created that can identify groupings and boundaries in images, including curves, corners, and junctions, offering a detailed, non-rasterized representation of the geometric structure in every local area.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":4,"score":31,"scale":"shares"},{"title":"Assumption-lean and Data-adaptive Post-Prediction Inference","url":"/papers/arxiv/2311.14220/","summary":"The PoSt-Prediction Adaptive inference (PSPA) method has been introduced to enable reliable and efficient inference based on machine learning-predicted data, ensuring dependable statistical inference regardless of the prediction's accuracy.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":42,"score":25,"scale":"shares"},{"title":"GMISeg: General Medical Image Segmentation without Re-Training","url":"/papers/arxiv/2311.12539/","summary":"GMISeg, a universal model for medical image segmentation, can handle new tasks involving unfamiliar anatomical structures or labels without extra training, simplifying the deployment of pre-trained AI models for new segmentation tasks.","featured":"2024-09-18","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":12,"scale":"shares"},{"title":"AI in Finance","url":"/papers/repec/wsi-wsbook-q0449/","summary":"The book provides insights into the role of artificial intelligence and machine learning in finance, linking their development to the human aspiration for automation.","featured":"2024-09-18","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Deep Learning for Economists","url":"/papers/arxiv/2407.15339/","summary":"The review discusses how deep learning methods can be used to extract structured information from large, unstructured datasets, with potential applications in economics.","featured":"2024-09-18","label":"arXiv","topic":"ML & AI Methods","cites":49,"score":826,"scale":"shares"},{"title":"Effective Stiffness of RC Hollow Columns","url":"/papers/ssrn/4951423/","summary":"Machine learning is utilized to estimate the stiffness of reinforced concrete hollow piers, using data from previous studies to train the predictive models.","featured":"2024-09-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"ML Prediction of Yield Strength in Stainless Steels","url":"/papers/ssrn/4949077/","summary":"Machine learning, specifically the Gradient Boosting model, is effectively used to predict the yield strength of irradiated type 316 stainless steels.","featured":"2024-09-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"TrainDB: Query Processing with ML Models","url":"/papers/ssrn/4947841/","summary":"Query Processing with ML Models: TrainDB, a machine learning-based query processing engine, is launched to provide approximate results for aggregate queries in large or sensitive datasets.","featured":"2024-09-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Board Diversity Performance","url":"/papers/ssrn/4947640/","summary":"The study uses machine learning to measure board diversity appearance, indicating that firms with visually diverse boards and less public attention generally perform better.","featured":"2024-09-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Mitigating AI Crashes","url":"/papers/ssrn/4950688/","summary":"A study explores the pros and cons of merging high-frequency trading with artificial intelligence, proposing regulatory steps to ensure market stability.","featured":"2024-09-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Predicting Future Price Movements with Bellwether Trades","url":"/papers/ssrn/4950344/","summary":"A study using nonlinear machine learning methods found that an optimized neural network predictor can accurately predict future market movements.","featured":"2024-09-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Topological Methods in Machine Learning: A Tutorial for Practitioners","url":"/papers/arxiv/2409.02901/","summary":"The tutorial gives an in-depth look at Topological Machine Learning, specifically persistent homology and the Mapper algorithm, with practical examples and applications.","featured":"2024-09-10","label":"Machine learning","topic":"ML & AI Methods","cites":22,"score":72,"scale":"shares"},{"title":"Quantum kernel methods under scrutiny: a benchmarking study","url":"/papers/arxiv/2409.04406/","summary":"The study conducts a large-scale analysis of quantum kernel methods in quantum machine learning, comparing different types of kernels to understand what makes them effective.","featured":"2024-09-10","label":"Machine learning","topic":"ML & AI Methods","cites":72,"score":11,"scale":"shares"},{"title":"$\\mu$GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning","url":"/papers/arxiv/2312.17293/","summary":"Bayesian Framework for Imaging: The research introduces a Bayesian framework, $\\mu$GUIDE, for estimating tissue microstructure parameters from any biophysical model or MRI signal, providing a solution to the high computational and time cost of traditional Bayesian methods.","featured":"2024-09-10","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":49,"scale":"shares"},{"title":"Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator","url":"/papers/arxiv/2407.06099/","summary":"A physics-informed machine learning model has been introduced for modeling thermal states in complex space missions, offering better generalization and reduced computing costs compared to traditional models.","featured":"2024-09-10","label":"Machine learning","topic":"ML & AI Methods","cites":10,"score":19,"scale":"shares"},{"title":"Urban Flood Mapping in New Orleans","url":"/papers/repec/spr-nathaz-v-120-y-2024-i-11-d-10-1007-s11069-024-06609-x/","summary":"The research finds that machine learning models, particularly the Random Forest model, outperform traditional statistical models in mapping flood susceptibility in New Orleans.","featured":"2024-09-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Machine Learning for Tech Analysis","url":"/papers/repec/kap-fmktpm-v-38-y-2024-i-3-d-10-1007-s11408-024-00451-8/","summary":"The research uses machine learning to predict daily stock returns, finding improved performance with feature selection.","featured":"2024-09-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":30,"scale":"shares"},{"title":"Financial Fraud Detection with Machine Learning","url":"/papers/repec/pal-palcom-v-11-y-2024-i-1-d-10-1057-s41599-024-03606-0/","summary":"The study reviews literature on financial fraud detection using machine learning, noting a trend towards real datasets and credit card fraud detection models.","featured":"2024-09-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh","url":"/papers/arxiv/2409.02201/","summary":"The article introduces a new method for evaluating the impact of new technologies in data-poor areas, combining Earth observation data, machine learning, and socioeconomic surveys, demonstrated through the case of stress-tolerant rice in Bangladesh.","featured":"2024-09-10","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"AI in Wealth Management","url":"/papers/ssrn/4943711/","summary":"The paper explores the role of AI and machine learning in enhancing wealth management by optimizing investment returns and risk management.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"AIDriven Investment Strategies","url":"/papers/ssrn/4944243/","summary":"The article examines the failures of AI in investment strategies, proposing network data analysis as a solution to build stronger AI systems.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Hybrid Machine Learning for Financial Distress","url":"/papers/ssrn/4941751/","summary":"The research uses hybrid machine learning models and balanced data to increase the accuracy of predicting financial distress, with the hybrid model outperforming the single model.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Transformers in AI Research","url":"/papers/ssrn/4945556/","summary":"The paper examines the structure of Transformers, large language models, and their potential and challenges in patent research.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Top 0 Cited Articles in Soft Computing AI Journal","url":"/papers/ssrn/4941452/","summary":"The paper explores the applications and challenges of Big Data, including various types of data analytics, with a focus on India.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI in Software Eng.","url":"/papers/ssrn/4943407/","summary":"The piece highlights how AI has transformed software engineering, improving efficiency, accuracy, and adaptability in areas like defect detection and software testing.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Machine Learning Scale","url":"/papers/ssrn/4940882/","summary":"The article explores the challenges and solutions in scaling machine learning systems, emphasizing on high throughput and reliability with large data sets.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Breaking Network Barriers VC","url":"/papers/ssrn/4941953/","summary":"The article notes that US Venture Capital activity is increasingly using digital data and machine learning to guide investment decisions, leading to more investments outside major hubs.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Harnessing Machine Learning and Mathematical Algorithms for Enhanced Real Estate Marketing Insights","url":"/papers/ssrn/4943278/","summary":"The paper examines the use of machine learning and mathematical algorithms in real estate for advanced marketing strategies.","featured":"2024-09-05","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"A General Framework for Optimizing and Learning Nash Equilibrium","url":"/papers/arxiv/2408.16260/","summary":"A new framework for optimizing and learning Nash equilibrium uses neural networks to estimate players' cost functions, with two methods proposed based on data availability, and tested in numerical experiments.","featured":"2024-09-05","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":3,"scale":"shares"},{"title":"Generative AI enables medical image segmentation in ultra low-data regimes","url":"/papers/arxiv/2408.17421/","summary":"A new deep learning framework generates high-quality medical images and segmentation masks, improving model performance in data-limited situations and reducing the need for extensive training data.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":20,"score":18,"scale":"shares"},{"title":"Text-to-Speech for Unseen Speakers via Low-Complexity Discrete Unit-Based Frame Selection","url":"/papers/arxiv/2408.17432/","summary":"SelectTTS, a new method for multi-speaker text-to-speech, uses self-supervised learning to capture speaker characteristics, reducing model complexity and training data while improving performance.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":5,"scale":"shares"},{"title":"Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective","url":"/papers/arxiv/2402.03496/","summary":"The research explores the performance of adaptive gradient optimizers without the square root, showing they maintain performance on transformers and improve on convolutional architectures.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":27,"score":45,"scale":"shares"},{"title":"ReconX: Reconstruct Any Scene From Sparse Views With Video Diffusion Model","url":"/papers/arxiv/2408.16767/","summary":"3D Scene Reconstruction: The paper presents ReconX, a new 3D scene reconstruction method using pre-trained video diffusion models, proving its superior quality and generalizability over existing methods.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":153,"score":33,"scale":"shares"},{"title":"A Hybrid Quantum-classical Fusion Neural Network to Improve Protein-ligand Binding Affinity Predictions for Drug Discovery","url":"/papers/arxiv/2309.03919/","summary":"A new quantum-classical deep learning model has been created to predict drug binding affinity, increasing accuracy by 6% and providing more stable performance than traditional models.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":16,"score":25,"scale":"shares"},{"title":"FilFL: Client Filtering for Optimized Client Participation in Federated Learning","url":"/papers/arxiv/2302.06599/","summary":"Client filtering, a new method to enhance model generalization and client participation in federated learning, has been proposed, leading to better learning efficiency, quicker convergence, and up to 10% increased test accuracy.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":23,"scale":"shares"},{"title":"A Survey on Responsible Generative AI: What to Generate and What Not","url":"/papers/arxiv/2404.05783/","summary":"The paper explores the responsible requirements of generative AI models, highlighting five key considerations and emphasizing the significance of responsible GenAI across various fields, with the aim to shed light on practical safety-related issues.","featured":"2024-09-05","label":"Machine learning","topic":"ML & AI Methods","cites":14,"score":17,"scale":"shares"},{"title":"Transfer Learning for Data-Scarce ML","url":"/papers/repec/cup-polals-v-32-y-2024-i-1-p-84-100-6/","summary":"Deep transfer learning models like BERT can greatly enhance the analysis of large political text corpora in social sciences research by reducing the need for extensive manually annotated training data.","featured":"2024-09-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Autoregressive Random Forests for Financial Research","url":"/papers/repec/kap-compec-v-64-y-2024-i-1-d-10-1007-s10614-023-10429-9/","summary":"The paper shows the effectiveness of Random Regression Forests for optimal lag selection in data series, outperforming other methods.","featured":"2024-09-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Machine Learning for FX Prediction","url":"/papers/ssrn/4937353/","summary":"Research combining foreign exchange rate forecasting with machine learning finds that machine learning models can predict currency variations more accurately than traditional models.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Data Resource Impact on Green Growth","url":"/papers/ssrn/4935262/","summary":"A study using Double Machine Learning methods on Chinese cities from 2000 to 2021 finds that a 1% increase in data resources correlates with a 2.1% rise in inclusive green growth, driven mainly by talent.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"GasLiquid Flow Analysis","url":"/papers/ssrn/4935257/","summary":"A study suggests that Distributed Acoustic Sensing (DAS) can be used for long-term monitoring of gas and liquid flow rates in pipelines, with machine learning improving the precision of predictions and classifications.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"American-style Contingent Claims Pricing","url":"/papers/ssrn/4937659/","summary":"The study uses indifference pricing and dynamic convex risk measures to determine the pricing of American style contingent claims, using solutions of Backward Stochastic Differential Equations and deep learning.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Utilizing Machine Learning to Enhance Cash Flow Management in SAP Finance Surya Sai Ram Parimi","url":"/papers/ssrn/4934859/","summary":"A survey discusses the potential benefits and challenges of integrating machine learning techniques into SAP Finance systems for improved cash flow management.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"AI Fraud Detection in Financial Crimes","url":"/papers/ssrn/4938417/","summary":"The advent of digital banking has escalated risks like fraud and identity theft, prompting financial institutions to adopt Machine Learning and AI for efficient fraud detection.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Predicting Price Movements","url":"/papers/ssrn/4936640/","summary":"The study uses machine learning to identify trade traits for accurate prediction of market trends.","featured":"2024-08-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Empirical Equilibria in Agent-based Economic systems with Learning agents","url":"/papers/arxiv/2408.12038/","summary":"The research introduces an agent-based simulator for economic systems, utilizing OpenAI Gym-style environment and PSRO algorithm, to combine AI, economics, and game theory for future studies.","featured":"2024-08-28","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":7,"scale":"shares"},{"title":"xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations","url":"/papers/arxiv/2408.12590/","summary":"Video Synthesis: xGen-VideoSyn-1 is a text-to-video generation model that creates realistic scenes from text descriptions using a video variational autoencoder and a Diffusion Transformer model.","featured":"2024-08-28","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":30,"scale":"shares"},{"title":"Jamba-1.5: Hybrid Transformer-Mamba Models at Scale","url":"/papers/arxiv/2408.12570/","summary":"Transformer-Mamba Models: Jamba-1.5 is a new large language model with enhanced conversational and instruction-following capabilities, featuring a unique quantization technique for cost-effective inference.","featured":"2024-08-28","label":"Machine learning","topic":"ML & AI Methods","cites":56,"score":17,"scale":"shares"},{"title":"ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction","url":"/papers/arxiv/2408.12598/","summary":"A paper introduces ND-SDF, a method that uses a Normal Deflection field to enhance the accuracy of 3D surface reconstruction, preserving geometric details and improving intricate surfaces.","featured":"2024-08-28","label":"Machine learning","topic":"ML & AI Methods","cites":11,"score":12,"scale":"shares"},{"title":"Data Quality Antipatterns for Software Analytics","url":"/papers/arxiv/2408.12560/","summary":"A study reveals that the sequence of cleaning data quality antipatterns significantly impacts the performance and interpretation of machine learning models in software analytics.","featured":"2024-08-28","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":12,"scale":"shares"},{"title":"Employing artificial intelligence to steer exascale workflows with colmena","url":"/papers/arxiv/2408.14434/","summary":"The paper introduces Colmena, an AI system that adapts and optimizes computational workflows on supercomputers, improving performance in various scientific fields and encouraging the use of AI in scientific computing.","featured":"2024-08-28","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":7,"scale":"shares"},{"title":"Critique-out-Loud Reward Models","url":"/papers/arxiv/2408.11791/","summary":"The article presents CLoud reward models that use human feedback to improve reinforcement learning, enhancing accuracy and win rate in ArenaHard.","featured":"2024-08-28","label":"Machine learning","topic":"ML & AI Methods","cites":99,"score":46,"scale":"shares"},{"title":"Arctic Ocean Sea Ice Prediction","url":"/papers/ssrn/4927619/","summary":"The study introduces machine learning strategies informed by physics to predict sea ice velocity and concentration in the Arctic Ocean, performing better than purely data-driven models, particularly during rapid melting and freezing periods.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Probabilistic RC Column Failure Prediction Model","url":"/papers/ssrn/4932324/","summary":"The paper presents a machine learning-based probabilistic prediction model to estimate the failure modes of reinforced concrete columns under earthquake load, taking into account the uncertainty due to randomness in column members and dynamic loads.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Adaptive Authentication Using Machine Learning","url":"/papers/ssrn/4932261/","summary":"The paper presents a machine learning system that uses past user login data to detect and block suspicious login attempts.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Corporate Social Value Measurement with Machine Learning","url":"/papers/ssrn/4932638/","summary":"The study employs a semi-supervised machine learning model to assess the social value of Chinese state-owned enterprises, showing an upward trend and notable differences across sectors and years.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Cloud Regression Testing Optimization","url":"/papers/ssrn/4927238/","summary":"The research shows that machine learning and cloud computing significantly enhance scalability and reduce testing time in software regression testing.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Securing Telematics Data in Fleet Management: Integrating IAM with ML Models for Data Integrity in Cloud-Based Applications","url":"/papers/ssrn/4927214/","summary":"The article explores the use of Identity Access Management (IAM) and machine learning to improve data security in cloud-based fleet management applications.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":5,"score":2,"scale":"shares"},{"title":"AI-Driven Fleet Financing: Transparent, Flexible, and Upfront Pricing for Smarter Decisions","url":"/papers/ssrn/4927194/","summary":"The article examines the use of artificial intelligence in transforming fleet financing, with a focus on fair pricing and improving decision-making in real-time marketplaces.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":7,"score":2,"scale":"shares"},{"title":"AI-Powered Predictive Thread Deadlock Resolution: An Intelligent System for Early Detection and Prevention of Thread Deadlocks in Cloud Applications","url":"/papers/ssrn/4927208/","summary":"The paper addresses the issue of thread deadlocks in cloud-based applications, suggesting an AI and machine learning system to predict and manage these deadlocks, improving system reliability.","featured":"2024-08-21","label":"SSRN","topic":"ML & AI Methods","cites":8,"score":2,"scale":"shares"},{"title":"Optimal stopping and divestment timing under scenario ambiguity and learning","url":"/papers/arxiv/2408.09349/","summary":"The research analyzes the effect of environmental transition on asset value, using a decision-making model to determine optimal divestment decisions.","featured":"2024-08-21","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Capturing the complexity of human strategic decision-making with machine learning","url":"/papers/arxiv/2408.07865/","summary":"A study reveals that a deep neural network can predict human decisions in two-player matrix games better than existing theories, with human response varying based on game complexity.","featured":"2024-08-21","label":"arXiv","topic":"ML & AI Methods","cites":22,"score":4,"scale":"shares"},{"title":"How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning","url":"/papers/arxiv/2408.10359/","summary":"The study highlights the crucial role of open labeled datasets like CIFAR-10 in the rise of Deep Learning, emphasizing its impact on computer vision, object recognition, and its importance in teaching machine learning techniques.","featured":"2024-08-21","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Deep-MacroFin: Informed Equilibrium Neural Network for Continuous-Time Economic Models","url":"/papers/arxiv/2408.10368/","summary":"Deep-MacroFin, a new framework that employs deep learning to solve complex equations in economics, is introduced, providing a more user-friendly alternative to existing tools.","featured":"2024-08-21","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"When and Why is Persuasion Hard? A Computational Complexity Result","url":"/papers/arxiv/2408.07923/","summary":"The study presents a mathematical model for informational persuasion, offering a theoretical foundation for examining AI's influence on industries and explaining why people can be persuaded even when all information is publicly accessible.","featured":"2024-08-21","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":2,"scale":"shares"},{"title":"Explore-then-Commit Algorithms for Decentralized Two-Sided Matching Markets","url":"/papers/arxiv/2408.08690/","summary":"Explore-then-Commit Algorithm: The article introduces a new algorithm for online learning in decentralized two-sided matching markets. This algorithm doesn't need prior knowledge of preference rankings or agent communication and effectively minimizes player regret.","featured":"2024-08-21","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":4,"scale":"shares"},{"title":"Scaling Law with Learning Rate Annealing","url":"/papers/arxiv/2408.11029/","summary":"The study introduces a scaling law for predicting the loss of neural language model training at any step and learning rate, aiding researchers in choosing optimal learning rate schedules.","featured":"2024-08-21","label":"Machine learning","topic":"ML & AI Methods","cites":31,"score":161,"scale":"shares"},{"title":"NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction","url":"/papers/arxiv/2408.10178/","summary":"NeuRodin, a two-stage neural surface reconstruction framework, is introduced, providing high-quality surface reconstruction while maintaining the optimization flexibility of density-based methods.","featured":"2024-08-21","label":"Machine learning","topic":"ML & AI Methods","cites":20,"score":18,"scale":"shares"},{"title":"Atmospheric Transport Modeling of CO$_2$ with Neural Networks","url":"/papers/arxiv/2408.11032/","summary":"The research investigates the use of four deep neural networks for atmospheric tracer transport modeling, with the SwinTransformer demonstrating excellent emulation capabilities for multi-year forward runs.","featured":"2024-08-21","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":15,"scale":"shares"},{"title":"A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning","url":"/papers/arxiv/2311.00212/","summary":"A new framework has been introduced to integrate symmetry into machine learning models, enabling the enforcement of known symmetries, discovery of unknown ones, and promotion of symmetry during training.","featured":"2024-08-21","label":"Machine learning","topic":"ML & AI Methods","cites":37,"score":796,"scale":"shares"},{"title":"Mechanistic Design and Scaling of Hybrid Architectures","url":"/papers/arxiv/2403.17844/","summary":"The creation of deep learning architectures can be streamlined using an end-to-end design pipeline, which uses synthetic tasks to predict scaling laws and find optimal architectures.","featured":"2024-08-21","label":"Machine learning","topic":"ML & AI Methods","cites":66,"score":130,"scale":"shares"},{"title":"Financial Markets Analysis with Deep Learning","url":"/papers/ssrn/4921331/","summary":"The article proposes a comparative analysis of deep learning techniques for financial market analysis, emphasizing the need for more detailed research in this field.","featured":"2024-08-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Stock Return Prediction with Big Data","url":"/papers/ssrn/4925722/","summary":"A two-stage quantile neural network and spline interpolation method can accurately predict stock returns based on 194 stock characteristics and market variables, performing better than other models.","featured":"2024-08-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning Exploration","url":"/papers/ssrn/4925203/","summary":"Machine learning is revolutionizing smart search and data discovery, with uses in voice search, predictive analytics, etc., but issues persist in data training, domain expertise, and ethical aspects.","featured":"2024-08-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Generative AI Advancements","url":"/papers/ssrn/4922568/","summary":"Machine learning and deep learning are propelling the progress of generative AI models, enhancing human-machine interaction and language understanding.","featured":"2024-08-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optimization Algorithms in Deep Learning","url":"/papers/ssrn/4925169/","summary":"The paper assesses various optimization algorithms for machine learning models, showing that Adam and RMSprop optimizers are effective for practical deep learning issues.","featured":"2024-08-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Why Groups Matter: Necessity of Group Structures in Attributions","url":"/papers/arxiv/2408.05701/","summary":"The study highlights the need to consider group structures in financial datasets when using explainable machine learning methods, advocating for group versions of the Shapley value for consistent explanations.","featured":"2024-08-15","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":3,"scale":"shares"},{"title":"Measuring and Controlling Fishing Capacity for Chinese Inshore Fleets","url":"/papers/arxiv/2408.05653/","summary":"The DEA method suggests that a tax system could control fishing capacity and boost efficiency in Chinese inshore fleets, provided the tax rate isn't too low.","featured":"2024-08-15","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations","url":"/papers/arxiv/2408.05620/","summary":"A new forward differential deep learning-based algorithm has been created to solve complex nonlinear backward stochastic differential equations, proving more efficient in accuracy and computation time.","featured":"2024-08-15","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals","url":"/papers/arxiv/2408.06679/","summary":"The study explores Explainable Case-Based Reasoning methods to clarify the results of black-box machine learning algorithms, using a technique to extract the learned distance metric from Random Forests, and assesses their explanatory power.","featured":"2024-08-15","label":"arXiv","topic":"ML & AI Methods","cites":5,"score":4,"scale":"shares"},{"title":"Body Transformer: Leveraging Robot Embodiment for Policy Learning","url":"/papers/arxiv/2408.06316/","summary":"The Body Transformer (BoT) architecture enhances robot learning by representing the robot's body as a sensor and actuator graph, proving more efficient than traditional transformers and multilayer perceptrons.","featured":"2024-08-15","label":"Machine learning","topic":"ML & AI Methods","cites":49,"score":58,"scale":"shares"},{"title":"ECG-FM: An Open Electrocardiogram Foundation Model","url":"/papers/arxiv/2408.05178/","summary":"ECG-FM, a transformer-based model for ECG analysis, shows strong performance in predicting cardiac conditions, having been pretrained on 2.5 million samples.","featured":"2024-08-15","label":"Machine learning","topic":"ML & AI Methods","cites":121,"score":55,"scale":"shares"},{"title":"EqNIO: Subequivariant Neural Inertial Odometry","url":"/papers/arxiv/2408.06321/","summary":"A new framework improves 2D displacement estimation from Inertial Measurement Unit data by addressing the overlooked symmetry principle, proving effective on various datasets.","featured":"2024-08-15","label":"Machine learning","topic":"ML & AI Methods","cites":25,"score":14,"scale":"shares"},{"title":"Decoding Quantum LDPC Codes Using Graph Neural Networks","url":"/papers/arxiv/2408.05170/","summary":"A new decoding method for Quantum Low-Density Parity-Check codes based on Graph Neural Networks outperforms conventional and neural-enhanced decoding algorithms in performance and complexity.","featured":"2024-08-15","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":6,"scale":"shares"},{"title":"CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning","url":"/papers/arxiv/2305.08057/","summary":"Machine Learning for Macrocycle Peptides: CREMP, a new dataset with over 36,000 unique macrocyclic peptides, is launched to support the development of machine learning models for peptide design and optimization in therapeutics.","featured":"2024-08-15","label":"Machine learning","topic":"ML & AI Methods","cites":23,"score":23,"scale":"shares"},{"title":"The Distributional Uncertainty of the SHAP score in Explainable Machine Learning","url":"/papers/arxiv/2401.12731/","summary":"A novel framework is proposed for reasoning on SHAP scores under unknown entity population distributions, enhancing feature scoring robustness in machine learning models.","featured":"2024-08-15","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":20,"scale":"shares"},{"title":"Impact of Personality Traits on Private Pension Participation","url":"/papers/repec/ist-journl-v-73-y-2024-i-1-p-281-314/","summary":"The study uses machine learning to examine the influence of personality traits and financial literacy on Private Pension System participation, highlighting significant factors like gender, age, and financial literacy.","featured":"2024-08-15","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Machine Learning and Stock Market Index","url":"/papers/ssrn/4913961/","summary":"A novel method combining machine learning and Monte Carlo simulation significantly improves returns in Chinese A-share markets, surpassing existing benchmarks.","featured":"2024-08-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Validity of Post hoc Explanations","url":"/papers/ssrn/4915307/","summary":"The research explores the effectiveness of post hoc explainers, SHAP and LIME, in determining the significance of variables in machine learning models, questioning their accuracy in revealing the real marginal effects of these variables.","featured":"2024-08-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for Lattice","url":"/papers/ssrn/4916881/","summary":"The research introduces a machine-learning method for estimating the lattice constants of double perovskite materials, utilizing algorithms such as Support Vector Regression, Artificial Neural Networks, Gaussian Process Regression, and Ensemble Regression Tree methods.","featured":"2024-08-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Sparrow Search for Customer Retention","url":"/papers/ssrn/4917418/","summary":"A new technique using machine learning models has been developed to predict and classify customer churn.","featured":"2024-08-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Customer Churn Prediction in Telecom","url":"/papers/ssrn/4912614/","summary":"Telecom operators are using a combination of customer segmentation and churn prediction, aided by four machine learning classifiers, to understand and retain customers at risk of leaving.","featured":"2024-08-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Stock Market Prediction with DL","url":"/papers/ssrn/4914504/","summary":"The paper finds that Long Short-Term Memory networks are more accurate and reliable than Recurrent Neural Networks in predicting stock prices.","featured":"2024-08-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities","url":"/papers/arxiv/2408.01499/","summary":"The research presents NeuralFactors, a machine-learning method for factor analysis that improves performance and efficiency in stock embedding.","featured":"2024-08-07","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":6,"scale":"shares"},{"title":"NeuralBeta: Estimating Beta Using Deep Learning","url":"/papers/arxiv/2408.01387/","summary":"Estimating Beta: A new method, NeuralBeta, uses neural networks to estimate beta in finance, capable of handling both single and multiple variable scenarios and tracking beta's dynamic behavior.","featured":"2024-08-07","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":20,"scale":"shares"},{"title":"Quantile Regression using Random Forest Proximities","url":"/papers/arxiv/2408.02355/","summary":"The article introduces a new method for calculating quantile regressions from random forests, showing improved performance and efficiency in predicting the average daily volume of corporate bonds.","featured":"2024-08-07","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":3,"scale":"shares"},{"title":"TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models","url":"/papers/arxiv/2408.00735/","summary":"Text-Based Image Editing: The study proposes a shifted noise schedule and a pseudo-guidance approach to address visual artifacts and insufficient editing strength in text-based image editing frameworks, enabling editing with minimal diffusion steps.","featured":"2024-08-07","label":"Machine learning","topic":"ML & AI Methods","cites":80,"score":35,"scale":"shares"},{"title":"An introduction to reinforcement learning for neuroscience","url":"/papers/arxiv/2311.07315/","summary":"The review explores the development of reinforcement learning in neuroscience, drawing comparisons between machine learning techniques and neuroscience, and introduces modern deep reinforcement learning methods.","featured":"2024-08-07","label":"Machine learning","topic":"ML & AI Methods","cites":10,"score":174,"scale":"shares"},{"title":"Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders","url":"/papers/arxiv/2407.14435/","summary":"The paper presents JumpReLU Sparse Autoencoders (SAEs), which provide high-quality reconstruction of language model activations at a specific sparsity level, while maintaining interpretability.","featured":"2024-08-07","label":"Machine learning","topic":"ML & AI Methods","cites":305,"score":125,"scale":"shares"},{"title":"ShieldGemma: Generative AI Content Moderation Based on Gemma","url":"/papers/arxiv/2407.21772/","summary":"ShieldGemma is a safety content moderation model that excels in predicting safety risks such as explicit content and hate speech, surpassing models like LlamaGuard and WildCard.","featured":"2024-08-07","label":"Machine learning","topic":"ML & AI Methods","cites":245,"score":55,"scale":"shares"},{"title":"Grappa – a machine learned molecular mechanics force field","url":"/papers/arxiv/2404.00050/","summary":"Grappa is a machine learning framework that accurately and efficiently predicts molecular mechanics parameters from molecular graphs, facilitating improved biomolecular simulations.","featured":"2024-08-07","label":"Machine learning","topic":"ML & AI Methods","cites":25,"score":44,"scale":"shares"},{"title":"iMatching","url":"/papers/arxiv/2312.02141/","summary":"Imperative learning (IL) is a new self-supervised scheme that enhances feature correspondence learning on any uninterrupted videos without requiring camera pose or depth labels, improving tasks like feature matching and pose estimation.","featured":"2024-08-07","label":"Machine learning","topic":"ML & AI Methods","cites":null,"score":36,"scale":"shares"},{"title":"Multicriteria Optimization for Deep Learning","url":"/papers/repec/spr-annopr-v-339-y-2024-i-1-d-10-1007-s10479-022-04833-x/","summary":"The study introduces a new machine learning model that reduces bias and minimizes loss function in data sets, successfully tested on digit classification.","featured":"2024-08-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":25,"scale":"shares"},{"title":"Improved Crayfish Optimization for Feature Selection","url":"/papers/repec/gam-jmathe-v-12-y-2024-i-15-p-2364-d-1445421/","summary":"The newly developed Improved Binary Crayfish Optimization Algorithm (IBCOA) enhances feature selection in data mining and machine learning, thus improving classification accuracy.","featured":"2024-08-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":19,"scale":"shares"},{"title":"Machine Learning in Business","url":"/papers/repec/pal-jintbs-v-55-y-2024-i-6-d-10-1057-s41267-024-00687-6/","summary":"The use of machine learning techniques in international business can address complexity and aid theory development, as per an article that also offers practical advice for implementing a machine learning process pipeline.","featured":"2024-08-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Predictor Variables with Random Forests","url":"/papers/repec/sae-jedbes-v-49-y-2024-i-4-p-595-629/","summary":"The study compares variable selection with random forests, a machine learning method, with linear models in behavioral sciences, providing practical advice.","featured":"2024-08-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Deep Learning for Newsvendor Problems","url":"/papers/repec/spr-annopr-v-339-y-2024-i-1-d-10-1007-s10479-024-05872-2/","summary":"The study uses a deep learning algorithm to effectively solve complex control models for supply and demand problems, financial risk management, and competitive scenarios, showing successful risk reduction.","featured":"2024-08-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":12,"scale":"shares"},{"title":"Property Uniqueness in Real Estate Sales","url":"/papers/repec/taf-rjrhxx-v-31-y-2022-i-2-p-220-240/","summary":"Machine learning can determine the uniqueness of residential properties from ads, which can increase sale prices but also lengthen market time.","featured":"2024-08-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Flexural Crack Width in Concrete Beams","url":"/papers/ssrn/4909625/","summary":"The research uses machine learning algorithms to predict the flexural crack width in reinforced concrete beams, identifying the Extra Gradient Boosting Regressor as the most accurate, and highlights the stress in reinforcing steel as a key influencing factor.","featured":"2024-07-31","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Quantum Machine Learning","url":"/papers/ssrn/4908091/","summary":"The paper highlights the superiority of quantum neural networks over traditional machine learning methods in monitoring geoenergy production systems, especially when dealing with limited and noisy data.","featured":"2024-07-31","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Data Filtering","url":"/papers/ssrn/4907901/","summary":"The review assesses the efficiency of machine learning algorithms in data filtering in fog, edge, and IoT environments, stressing the importance of data classification and the speed increase by eliminating false and noisy data.","featured":"2024-07-31","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"The Impact of the ESG Factor on Industrial Performance An Analysis using Machine Learning Techniques","url":"/papers/ssrn/4910370/","summary":"The study applies machine learning to explore the link between ESG performance and corporate earnings, using data from over 850 European and US firms from 2007-2021.","featured":"2024-07-31","label":"SSRN","topic":"ML & AI Methods","cites":4,"score":6,"scale":"shares"},{"title":"Automated Security for MLOps","url":"/papers/ssrn/4908612/","summary":"The article emphasizes the need for strong automated security in Machine Learning Operations (MLOps) to guard against various threats, and highlights the latest tools and trends in the field.","featured":"2024-07-31","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Business and Regulatory Responses to Artificial Intelligence: Dynamic Regulation, Innovation Ecosystems and the Strategic Management of Disruptive Technology","url":"/papers/arxiv/2407.19439/","summary":"The article explores the difficulties of incorporating AI into businesses, proposing dynamic regulation and innovation ecosystems as solutions, with Fintech as a case study.","featured":"2024-07-31","label":"arXiv","topic":"ML & AI Methods","cites":27,"score":3,"scale":"shares"},{"title":"Artificial intelligence and financial crises","url":"/papers/arxiv/2407.17048/","summary":"The financial sector is undergoing a transformation due to the swift adoption of AI, which could either stabilize the system or increase financial risk, and future crises may be more severe due to AI's quick reaction to shocks.","featured":"2024-07-31","label":"arXiv","topic":"ML & AI Methods","cites":9,"score":3,"scale":"shares"},{"title":"Theia: Distilling Diverse Vision Foundation Models for Robot Learning","url":"/papers/arxiv/2407.20179/","summary":"Robot Learning Vision Model: Theia is a robot learning model that uses multiple pre-trained vision models, enhancing robot learning with less data and smaller models.","featured":"2024-07-31","label":"Machine learning","topic":"ML & AI Methods","cites":78,"score":75,"scale":"shares"},{"title":"MindSearch: Mimicking Human Minds Elicits Deep AI Searcher","url":"/papers/arxiv/2407.20183/","summary":"Mimicking Human Minds for Search: MindSearch is a Large Language Model-based framework that simulates human cognitive processes for web information seeking, greatly enhancing response quality.","featured":"2024-07-31","label":"Machine learning","topic":"ML & AI Methods","cites":80,"score":44,"scale":"shares"},{"title":"Learning Random Numbers to Realize Appendable Memory System for Artificial Intelligence to Acquire New Knowledge after Deployment","url":"/papers/arxiv/2407.20197/","summary":"The Appendable Memory system is a new AI that can learn new information after its initial programming, unlike traditional machine learning methods.","featured":"2024-07-31","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":17,"scale":"shares"},{"title":"Emergence in non-neural models: grokking modular arithmetic via average gradient outer product","url":"/papers/arxiv/2407.20199/","summary":"The 'grokking' phenomenon, where a model's test accuracy improves after achieving 100% training accuracy, can also occur with Recursive Feature Machines, not just neural networks.","featured":"2024-07-31","label":"Machine learning","topic":"ML & AI Methods","cites":28,"score":14,"scale":"shares"},{"title":"Is artificial consciousness achievable? Lessons from the human brain","url":"/papers/arxiv/2405.04540/","summary":"The creation of artificial consciousness in AI should take into account the structural and functional aspects of the human brain; while it may not fully replicate human consciousness, AI could potentially develop different forms of consciousness.","featured":"2024-07-31","label":"Machine learning","topic":"ML & AI Methods","cites":37,"score":105,"scale":"shares"},{"title":"Marine accident severity prediction","url":"/papers/repec/eee-transe-v-188-y-2024-i-c-s1366554524002382/","summary":"The research presents a framework to predict the severity of marine accidents using machine learning models and a unique two-stage feature selection method.","featured":"2024-07-31","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Machine Learning for Real Estate Price Indices","url":"/papers/repec/kap-jrefec-v-68-y-2024-i-4-d-10-1007-s11146-022-09893-1/","summary":"The article introduces a machine learning methodology for creating property price indices, providing higher prediction accuracy but potentially biased estimations for small samples.","featured":"2024-07-31","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Flexible Truck Appointment System","url":"/papers/repec/ids-ijlsma-v-48-y-2024-i-2-p-244-266/","summary":"The paper proposes a machine learning model for flexible truck appointment systems in smart ports, using real-time data to identify disruptions and reschedule appointments, thus enhancing port efficiency.","featured":"2024-07-31","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"AI Adoption in Competitive Markets","url":"/papers/repec/bla-econom-v-90-y-2023-i-358-p-690-705/","summary":"The paper presents AI as a tool for improved prediction in competitive markets, demonstrating that AI use can increase supply elasticity, influence equilibrium prices, and potentially benefit non-adopting firms.","featured":"2024-07-31","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"Auditor Reliance on AI","url":"/papers/repec/bla-joares-v-60-y-2022-i-1-p-171-201/","summary":"The research explores the effect of algorithm aversion on auditor decisions, indicating that auditors tend to disregard advice from AI systems, which could be expensive for the auditing industry and financial statement users.","featured":"2024-07-31","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":73,"scale":"shares"},{"title":"Leverage Dynamics and Learning about Economic Crises","url":"/papers/ssrn/4898492/","summary":"The paper reconciles the conflict between risk premiums and belief uncertainty during economic crises using a model that includes leverage dynamics.","featured":"2024-07-24","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Intrusion Detection for IoV Security","url":"/papers/ssrn/4903760/","summary":"The paper introduces a new method for updating Intrusion Detection Systems in Internet of Vehicles applications using Convolutional Neural Networks to speed up the detection of recent attacks.","featured":"2024-07-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Optimizing Canal Operations","url":"/papers/ssrn/4903807/","summary":"A new tool that combines data science and machine learning can improve water management in large irrigation projects, ensuring fair distribution.","featured":"2024-07-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Shear Strength Prediction","url":"/papers/ssrn/4898878/","summary":"Machine learning models, specifically the Extreme Gradient Boosting model, can accurately predict the shear capacity of certain strengthened beams, aiding in better design practices.","featured":"2024-07-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Response to the U.S. Department of Commerce Request to Information on AI and Open Government Data Assets","url":"/papers/ssrn/4900823/","summary":"The article emphasizes the importance of open data for AI, but also warns about ethical issues and potential harms related to data availability.","featured":"2024-07-24","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Mafia Infiltration Prediction with Machine Learning","url":"/papers/ssrn/4901233/","summary":"The research uses machine learning to predict mafia infiltration in Italian local governments, offering a tool for early detection and understanding the causes of such crime.","featured":"2024-07-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"On deep learning for computing the dynamic initial margin and margin value adjustment","url":"/papers/arxiv/2407.16435/","summary":"The study introduces a method for training neural networks for Dynamic Initial Margin computation in counterparty credit risk, which reduces dataset generation costs and eliminates the need for repeated training.","featured":"2024-07-24","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Factor-Biased Efficiency Gains from Exporting: Evidence from Colombia","url":"/papers/arxiv/2407.14016/","summary":"The research creates a dynamic model of production, export, and capital investment, discovering that exporting plants significantly upgrade their technology, boosting total productivity and the productivity of both skilled and unskilled workers, especially for new exporters.","featured":"2024-07-24","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Prompt Adaptation as a Dynamic Complement in Generative AI Systems","url":"/papers/arxiv/2407.14333/","summary":"An online experiment with AI models DALL-E 2 and DALL-E 3 showed that as AI improves, users adapt their prompts to utilize new capabilities, with DALL-E 3 users giving longer, more detailed prompts.","featured":"2024-07-24","label":"arXiv","topic":"ML & AI Methods","cites":15,"score":4,"scale":"shares"},{"title":"Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management","url":"/papers/arxiv/2407.13751/","summary":"Stock Similarities Representation Learning: The paper introduces SimStock, a self-supervised learning framework for identifying similar stocks, which outperforms existing methods and can be used in various investment strategies, highlighting the potential of data-driven approaches in investment and risk management.","featured":"2024-07-24","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":13,"scale":"shares"},{"title":"ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery","url":"/papers/arxiv/2405.04657/","summary":"RL for Drug Design: ACEGEN, a toolkit for drug design using reinforcement learning, is presented and validated, demonstrating equal or better performance than other generative models.","featured":"2024-07-24","label":"Machine learning","topic":"ML & AI Methods","cites":41,"score":30,"scale":"shares"},{"title":"Risk Co-De Model","url":"/papers/repec/spr-jcsosc-v-7-y-2024-i-1-d-10-1007-s42001-023-00235-6/","summary":"The paper presents a machine learning model to classify social media posts by risk perception, aiding in understanding human risk approach and informing communication strategies.","featured":"2024-07-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Vaccination Impact on Mortality","url":"/papers/repec/oup-emjrnl-v-27-y-2024-i-2-p-299-322/","summary":"The paper uses double machine learning to estimate the impact of vaccination on COVID-19 mortality in the EU, finding that a 10% increase in doses significantly reduces deaths and that Moderna and AstraZeneca vaccines are more cost-effective than Pfizer.","featured":"2024-07-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":10,"scale":"shares"},{"title":"Phononic Crystal-Based pH Sensing & Machine Learning","url":"/papers/ssrn/4893619/","summary":"The study presents a 3D phononic crystal-based pH sensor, showcasing its sensitivity to pH changes and the effectiveness of machine learning for pH classification.","featured":"2024-07-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Healthcare Fraud Detection","url":"/papers/ssrn/4892805/","summary":"Machine learning techniques are being used to detect healthcare fraud in the US, with a study analyzing over 558,211 records using various ML models.","featured":"2024-07-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI Model Explainability","url":"/papers/ssrn/4894519/","summary":"The article discusses the legal challenges posed by the use of complex AI and machine learning models in decision-making processes due to their lack of traceability.","featured":"2024-07-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Representing Rule-based Chatbots with Transformers","url":"/papers/arxiv/2407.10949/","summary":"The research builds a Transformer that replicates the ELIZA program, a traditional rule-based chatbot, to gain insights into the preferred mechanisms of Transformer-based chatbots.","featured":"2024-07-17","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":19,"scale":"shares"},{"title":"Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms","url":"/papers/arxiv/2407.08723/","summary":"The research proposes a new set of topology-based complexity notions that correlate with the generalization gap in deep neural networks, offering a computationally efficient way to predict generalization without test data, and surpassing existing topological bounds across various datasets and models.","featured":"2024-07-17","label":"Machine learning","topic":"ML & AI Methods","cites":12,"score":15,"scale":"shares"},{"title":"Weight Block Sparsity: Training, Compilation, and AI Engine Accelerators","url":"/papers/arxiv/2407.09453/","summary":"The paper proposes a system that applies weight block sparsity in Deep Neural Networks, halving the weight with minimal accuracy loss and doubling the speed of inference.","featured":"2024-07-17","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":10,"scale":"shares"},{"title":"Transformer Circuit Faithfulness Metrics are not Robust","url":"/papers/arxiv/2407.08734/","summary":"The authors explore the difficulties in evaluating the performance of neural network 'circuits', emphasizing the sensitivity of current methods to changes in the ablation methodology and the need for clearer claims about circuits.","featured":"2024-07-17","label":"Machine learning","topic":"ML & AI Methods","cites":19,"score":9,"scale":"shares"},{"title":"Lie Group Decompositions for Equivariant Neural Networks","url":"/papers/arxiv/2310.11366/","summary":"The paper introduces a framework for dealing with Lie groups and their homogeneous spaces, showing how to parametrize convolution kernels to create models that are equivariant to affine transformations.","featured":"2024-07-17","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":60,"scale":"shares"},{"title":"Equity Investment Strategy with AI","url":"/papers/ssrn/4886903/","summary":"The study introduces an equity investment strategy that uses artificial intelligence, multi-factor models, and financial indicators to predict returns and mitigate risks.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Adaptive PET Prediction","url":"/papers/ssrn/4890471/","summary":"The research suggests an adaptive hybrid model using automatic machine learning for short-term PET prediction, showing that the effectiveness of neural networks varies depending on the data sources used.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Multi-Objective Injection Molded Parts Optimization","url":"/papers/ssrn/4887380/","summary":"The study applies a validated simulation of the plastic injection process and machine learning to estimate the necessary clamping force, optimizing key parameters and reducing defects and energy use in the pipes and fittings industry.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Graph-Based Frame Optimization Model","url":"/papers/ssrn/4888783/","summary":"The paper introduces a method for optimizing the topology of three-dimensional frames under static seismic loads using a hierarchical graph-based machine learning model, proving its effectiveness over traditional methods in optimizing large three-dimensional building frames.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning Study on Zeolite Catalysts","url":"/papers/ssrn/4886111/","summary":"The study uses both experimental and machine learning methods to examine how the structure of zeolites affects the isomerization of 1octene.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Alpha Plane Concept for Transformer Protection","url":"/papers/ssrn/4887338/","summary":"The paper suggests using the Alpha Plane concept and a new algorithm to improve transformer protection in electric power systems, with simulations conducted on MATLABSIMULINK.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Topological Data Analysis for Target Recognition","url":"/papers/ssrn/4885393/","summary":"The research introduces a new artificial intelligence-machine learning pipeline for automated target recognition using topological data analysis from a sensing grid’s multimodal data.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"ML for Power Outages Prediction","url":"/papers/ssrn/4886156/","summary":"The article discusses the importance of accurate machine learning models for predicting weather-induced power outages, to help utility companies minimize damage to the power system.","featured":"2024-07-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"GraphCNNpred: A stock market indices prediction using a Graph based deep learning system","url":"/papers/arxiv/2407.03760/","summary":"Stock Prediction: The paper introduces a graph neural network-based convolutional neural network model for predicting stock market prices, using custom feature engineering on diverse data sources.","featured":"2024-07-10","label":"arXiv","topic":"ML & AI Methods","cites":11,"score":3,"scale":"shares"},{"title":"Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets","url":"/papers/arxiv/2407.04088/","summary":"AI algorithms using Q-learning can encourage silent collusion in two-sided markets, increasing profits; a penalty term in the algorithm could reduce this.","featured":"2024-07-10","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":7,"scale":"shares"},{"title":"Embracing Massive Medical Data","url":"/papers/arxiv/2407.04687/","summary":"A new online learning method for AI training on large medical data sets has been proposed, improving efficiency and reducing data loss, with a 15% improvement in multi-organ and tumor segmentation.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":11,"score":14,"scale":"shares"},{"title":"Efficient Materials Informatics between Rockets and Electrons","url":"/papers/arxiv/2407.04648/","summary":"An AI-guided infrastructure is being developed to better understand and apply materials informatics, with potential applications in designing materials for gas turbines, jet engines, and hypersonic vehicles.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":11,"scale":"shares"},{"title":"JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation","url":"/papers/arxiv/2407.06187/","summary":"A new model, Joint-Image Diffusion, has been proposed for personalized text-to-image generation, which learns the joint distribution of multiple related text-image pairs, outperforming previous personalization models.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":66,"score":7,"scale":"shares"},{"title":"Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design","url":"/papers/arxiv/2407.06152/","summary":"Representation Learning for Electrolyte Design: The authors present Uni-ELF, a representation learning framework for electrolyte design, which surpasses existing methods in predicting molecular and formulation properties and can be incorporated into an automatic experimental design process.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":5,"scale":"shares"},{"title":"Stepping on the Edge: Curvature Aware Learning Rate Tuners","url":"/papers/arxiv/2407.06183/","summary":"The paper explores the link between learning rate tuning and curvature in machine learning, presenting a new method, Curvature Dynamics Aware Tuning (CDAT), which focuses on long-term curvature stabilization.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":4,"scale":"shares"},{"title":"XQSV: A Structurally Variable Network to Imitate Human Play in Xiangqi","url":"/papers/arxiv/2407.04678/","summary":"The article introduces Xiangqi Structurally Variable (XQSV), a deep learning architecture that mimics human behavior in Chinese Chess, achieving around 40% predictive accuracy and passing a three-terminal Turing Test.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"SimPO: Simple Preference Optimization with a Reference-Free Reward","url":"/papers/arxiv/2405.14734/","summary":"Simple Preference Optimization: SimPO improves reinforcement learning from human feedback by using the average log probability of a sequence as the implicit reward, enhancing training stability and computational efficiency.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":1173,"score":728,"scale":"shares"},{"title":"DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents","url":"/papers/arxiv/2407.03300/","summary":"Enhancing Diffusion Models: Discrete-Continuous Latent Variable Diffusion Models simplify encoding complex data into a Gaussian distribution, improving performance in image synthesis and molecular docking tasks.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":24,"score":73,"scale":"shares"},{"title":"Naturalistic Music Decoding from EEG Data via Latent Diffusion Models","url":"/papers/arxiv/2405.09062/","summary":"The article explores how latent diffusion models can recreate complex music from brainwave (EEG) recordings, aiding in brain-computer interface research.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":38,"scale":"shares"},{"title":"Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery","url":"/papers/arxiv/2303.15975/","summary":"The research proposes a new learning paradigm for continuous and unsupervised class discovery in class-iNCD, using self-supervised pre-trained models.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":13,"scale":"shares"},{"title":"Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises","url":"/papers/arxiv/2405.08698/","summary":"The article introduces ByITFL, a new Federated Learning scheme that protects against malicious users and ensures data privacy, marking the first Byzantine resilient scheme with full information-theoretic privacy.","featured":"2024-07-10","label":"Machine learning","topic":"ML & AI Methods","cites":18,"score":11,"scale":"shares"},{"title":"Feature Importance for Mixed Data","url":"/papers/repec/spr-alstar-v-108-y-2024-i-2-d-10-1007-s10182-023-00477-9/","summary":"The article proposes a new method in machine learning that combines the conditional predictive impact framework with sequential knockoff sampling to better distinguish between marginal and conditional measures in feature importance.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"AI Readiness Enablers in Economies","url":"/papers/repec/eee-tefoso-v-205-y-2024-i-c-s0040162524002786/","summary":"The study uses machine learning to identify factors affecting AI readiness in businesses across 40 nations, finding that AI readiness is more predictable in developing countries, with scientific research output, internet infrastructure, and public consumption expense as key factors.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Stock Index Prediction with Machine Learning","url":"/papers/repec/taf-raaexx-v-31-y-2024-i-4-p-618-637/","summary":"The research uses sentiment analysis and machine learning to predict stock indexes, highlighting the impact of investor sentiment and exchange rates on the Shanghai Composite Index.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Hyperparameter Tuning Efficiency","url":"/papers/repec/spr-alstar-v-108-y-2024-i-2-d-10-1007-s10182-024-00495-1/","summary":"The study presents the sequential random search (SQRS) for hyperparameter tuning in machine learning, which reduces computational effort by discarding inferior parameter configurations early.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Machine Learning for Real-World Issues","url":"/papers/repec/bao-jdaisn-v-2-y-2023-i-1-p-9-16-id-13/","summary":"Machine learning can forecast business trends using big data, but its integration necessitates major system changes, including data collection and workflow architecture.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":52,"scale":"shares"},{"title":"Predicting Earnings Changes","url":"/papers/repec/bla-joares-v-60-y-2022-i-2-p-467-515/","summary":"Machine learning models using extensive financial data can predict future earnings changes more accurately than traditional models and professional analysts.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":93,"scale":"shares"},{"title":"Transnational Bid-Rigging Detection","url":"/papers/repec/bla-jorssa-v-185-y-2022-i-3-p-1074-1114/","summary":"Bid-rigging cartels can be identified using statistical screening methods and machine learning, but their effectiveness varies across countries due to institutional differences.","featured":"2024-07-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":24,"scale":"shares"},{"title":"PLOD Predictive Learning for Data Discovery","url":"/papers/ssrn/4882133/","summary":"The PLOD algorithm, based on the BOD method, can accurately predict the desired utility function from data without needing the exact utility function, enhancing efficiency in data science and analytics.","featured":"2024-07-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Hybrid Machine Learning for Armor Prediction","url":"/papers/ssrn/4881827/","summary":"The article proposes a data-driven framework using a hybrid model of Support Vector Machine and Deep Neural Network for predicting the ballistic performance of composite armor, demonstrating high accuracy and generalizability.","featured":"2024-07-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"DataDriven Modeling for Self-Similar Dynamics","url":"/papers/ssrn/4878940/","summary":"The article introduces a multiscale neural network framework that incorporates selfsimilarity as prior knowledge, enabling the modeling of selfsimilar dynamical systems in complex systems.","featured":"2024-07-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Deep Learning for Automatic Text Summarization: Introductory part of Current Techniques","url":"/papers/ssrn/4878546/","summary":"The research provides an in-depth review of deep learning techniques for text summarization, including different structures, attention mechanisms, and recent evaluation metrics.","featured":"2024-07-03","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Machine Learning for Drug Accessibility","url":"/papers/ssrn/4879072/","summary":"Two machine learning models, MLPRegressor and Keras Sequential, have been created to predict the synthetic accessibility scores of molecules with a mean squared error of around 0.20.","featured":"2024-07-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Detection of Cyber Attacks on Network Using Machine Learning Techniques","url":"/papers/ssrn/4878268/","summary":"The research suggests using deep neural networks to identify deception attacks in cyber-physical systems that disrupt system performance by injecting false data.","featured":"2024-07-03","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors","url":"/papers/arxiv/2406.18394/","summary":"Formulaic Alpha Generation Framework: The paper introduces AlphaForge, a two-stage formulaic alpha generating framework that uses a generative-predictive neural network for factor generation and dynamic weight adjustment, showing improved performance in alpha factor mining.","featured":"2024-07-03","label":"arXiv","topic":"ML & AI Methods","cites":40,"score":6,"scale":"shares"},{"title":"PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators","url":"/papers/arxiv/2406.20083/","summary":"PoliFormer, an indoor navigation agent trained in simulation, performs well in real-world scenarios, showing potential for various applications.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":85,"score":61,"scale":"shares"},{"title":"HouseCrafter: Lifting Floorplans to 3D Scenes with 2D Diffusion Models","url":"/papers/arxiv/2406.20077/","summary":"3D Scene Gen: HouseCrafter, a new method, can transform a floorplan into a 3D indoor scene using a 2D diffusion model trained on large-scale web images.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":10,"score":7,"scale":"shares"},{"title":"Taming Data and Transformers for Audio Generation","url":"/papers/arxiv/2406.19388/","summary":"AutoCap and GenAu, two new models for generating ambient sounds and effects, are introduced, improving the quality of audio captions and generated audio.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":40,"score":6,"scale":"shares"},{"title":"Bytes Are All You Need: Transformers Operating Directly On File Bytes","url":"/papers/arxiv/2306.00238/","summary":"File Byte Transformers: ByteFormer is a deep learning model that enhances image classification accuracy by 5% and can perform audio classification without specific preprocessing, showcasing its versatility.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":19,"score":156,"scale":"shares"},{"title":"Fine-tuning can cripple your foundation model; preserving features may be the solution","url":"/papers/arxiv/2308.13320/","summary":"Concept forgetting in AI models can be significantly reduced by a new fine-tuning method called LDIFS, which helps retain pre-trained knowledge while working on different tasks.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":102,"score":75,"scale":"shares"},{"title":"Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout","url":"/papers/arxiv/2404.17701/","summary":"The study successfully demonstrates the use of embedded field programmable gate array (eFPGA) technology in machine learning for data pipelines in future collider experiments, achieving perfect accuracy.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":23,"scale":"shares"},{"title":"Explainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling","url":"/papers/arxiv/2404.18510/","summary":"The research investigates the explainability of machine learning in forensic authorship profiling, finding that lexical features and place names play a significant role in the classification process.","featured":"2024-07-03","label":"Machine learning","topic":"ML & AI Methods","cites":4,"score":20,"scale":"shares"},{"title":"XGBoost for LGD Approximation","url":"/papers/repec/war-wpaper-2024-12/","summary":"The study uses machine learning to enhance the accuracy of Loss Given Default (LGD) estimation in situations with limited cash-flow data, using a European mortgage portfolio.","featured":"2024-07-03","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":25,"scale":"shares"},{"title":"Machine Learning for Stock Selection","url":"/papers/repec/eee-riibaf-v-70-y-2024-i-pa-s0275531924001296/","summary":"The research finds that despite higher risks, machine learning algorithms can select a subset of stocks that outperform the S&P 500, with the importance of determining factors changing over time.","featured":"2024-07-03","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":24,"scale":"shares"},{"title":"GANs for Chemical Foam Detection in Low Data","url":"/papers/ssrn/4868138/","summary":"The research uses Generative Adversarial Networks (GANs) to improve the classification of chemical foam, addressing the issue of scarce and poorly labeled datasets in the chemical sector.","featured":"2024-06-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"MSBoost: Model Selection for Gradient Boosting","url":"/papers/ssrn/4867120/","summary":"Model Selection for Gradient Boosting: The paper introduces a new gradient boosting approach that trains multiple models on residual errors simultaneously, proving especially effective for small and noisy datasets.","featured":"2024-06-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Hands-Free Mouse System","url":"/papers/ssrn/4870525/","summary":"A Hands-Free Mouse system uses facial recognition and machine learning to improve accessibility for physically disabled individuals in the field of assistive technology.","featured":"2024-06-20","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Exploitation of Historical Analog Seismological Records by Image Processing and Machine Learning","url":"/papers/ssrn/4865699/","summary":"The project aims to vectorise old seismograms using machine learning, focusing on reducing human interaction and increasing programming approach in the process.","featured":"2024-06-20","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Can AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?","url":"/papers/arxiv/2406.11426/","summary":"The study examines the use of large language models in mimicking human decision-making in economic experiments, emphasizing the importance of the models' reasoning capabilities in achieving realistic results.","featured":"2024-06-20","label":"arXiv","topic":"ML & AI Methods","cites":18,"score":4,"scale":"shares"},{"title":"LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning","url":"/papers/arxiv/2406.11815/","summary":"Robot Learning Enhancement: The LLARVA model, trained with a new method, successfully unifies various robotic learning tasks and performs well in different robot environments.","featured":"2024-06-20","label":"Machine learning","topic":"ML & AI Methods","cites":96,"score":59,"scale":"shares"},{"title":"Stochastic Neural Network Symmetrisation in Markov Categories","url":"/papers/arxiv/2406.11814/","summary":"The study proposes a new framework for symmetrising neural networks using group homomorphism and Markov categories, demonstrating their usefulness in machine learning.","featured":"2024-06-20","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":55,"scale":"shares"},{"title":"Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation","url":"/papers/arxiv/2406.12815/","summary":"The research reviews Federated Learning in medical imaging, discussing its challenges, potential for privacy preservation and uncertainty estimation, and suggesting future research directions.","featured":"2024-06-20","label":"Machine learning","topic":"ML & AI Methods","cites":34,"score":13,"scale":"shares"},{"title":"An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels","url":"/papers/arxiv/2406.09415/","summary":"A study shows that vanilla Transformers can treat each pixel as a token and still perform well, questioning the need for locality in computer vision architectures and suggesting a new direction for future designs.","featured":"2024-06-20","label":"Machine learning","topic":"ML & AI Methods","cites":75,"score":228,"scale":"shares"},{"title":"HyperFields: Towards Zero-Shot Generation of NeRFs from Text","url":"/papers/arxiv/2310.17075/","summary":"HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs), is introduced, offering faster prediction of novel scenes and highlighting the significance of dynamic architecture and NeRF distillation.","featured":"2024-06-20","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":72,"scale":"shares"},{"title":"Accounting Journals: Research and Practice","url":"/papers/repec/eme-jalpps-jal-03-2023-0047/","summary":"Research and Practice: The study uses machine learning to highlight a significant gap between academic research and practical application in accounting, more so in the US than Europe.","featured":"2024-06-20","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Machine Learning for Phishing Certificate Detection","url":"/papers/ssrn/4855968/","summary":"A proposed system uses machine learning to detect phishing websites via their digital certificates, improving upon slower traditional methods.","featured":"2024-06-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"DRLDQN for DDoS Detection in SDN","url":"/papers/ssrn/4855737/","summary":"A Deep Q-Network (DQN) machine learning model has been created to predict cyberattacks in software-defined networking with greater accuracy and less training time.","featured":"2024-06-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Enconet: Speech Attribution with CNNs","url":"/papers/ssrn/4856794/","summary":"Speech Attribution with CNNs: EnCoNet, an ensemble-based synthetic speech attribution algorithm, has been proposed to distinguish between human and AI-generated speech using lightweight neural networks.","featured":"2024-06-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Clustering Data","url":"/papers/ssrn/4862221/","summary":"The research introduces a new equation for unsupervised data separation to improve clustering models in machine learning.","featured":"2024-06-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"China's Rising Leadership in Global Science","url":"/papers/arxiv/2406.05917/","summary":"A machine-learning study predicts that China will match the US, UK, and EU in scientific leadership by 2027-2028. However, it is not expected to reach parity in terms of per-collaborator until after 2087.","featured":"2024-06-12","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":5,"scale":"shares"},{"title":"Improving Alignment and Robustness with Circuit Breakers","url":"/papers/arxiv/2406.04313/","summary":"Circuit breakers is a new method proposed to stop AI systems from causing harm or producing damaging outputs, even under unseen powerful attacks.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":345,"score":497,"scale":"shares"},{"title":"BitsFusion: 1.99 bits Weight Quantization of Diffusion Model","url":"/papers/arxiv/2406.04333/","summary":"Weight Quantization: A new weight quantization method has been developed to reduce the size of diffusion-based image generation models by 7.9 times, while also improving the quality of the generated images.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":47,"score":86,"scale":"shares"},{"title":"SF-V: Single Forward Video Generation Model","url":"/papers/arxiv/2406.04324/","summary":"A new method for creating high-quality videos in a single step using adversarial training and pre-trained video diffusion models is proposed, reducing computational costs.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":42,"score":41,"scale":"shares"},{"title":"Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics","url":"/papers/arxiv/2406.05108/","summary":"A new neural network framework is proposed for solving forward and inverse problems in ordinary differential equation systems, demonstrated through a case study on mosquito population dynamics.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":18,"score":14,"scale":"shares"},{"title":"Self-Improving Robust Preference Optimization","url":"/papers/arxiv/2406.01660/","summary":"The Self-Improving Robust Preference Optimization (SRPO) framework aims to improve AI alignment robustness, using self-improvement to optimize policy independently of the training task.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":25,"score":381,"scale":"shares"},{"title":"Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction","url":"/papers/arxiv/2404.02905/","summary":"The article discusses Visual AutoRegressive modeling (VAR), a new image learning method that outperforms diffusion transformers in terms of speed, image quality, and scalability.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":1169,"score":257,"scale":"shares"},{"title":"Towards a theory of out-of-distribution learning","url":"/papers/arxiv/2109.14501/","summary":"The paper suggests a chronological approach to defining learning tasks using the PAC learning framework, aiming to standardize definitions for various learning paradigms.","featured":"2024-06-12","label":"Machine learning","topic":"ML & AI Methods","cites":22,"score":60,"scale":"shares"},{"title":"Financial Scenario Generation with Machine Learning","url":"/papers/repec/kap-compec-v-63-y-2024-i-5-d-10-1007-s10614-023-10387-2/","summary":"The article introduces a new machine learning method for predicting one-day-ahead scenarios for portfolio optimization, resulting in more accurate forecasts and less risky portfolios.","featured":"2024-06-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":27,"scale":"shares"},{"title":"Interpretable Machine Learning Recovery Rates","url":"/papers/repec/eee-jbfina-v-164-y-2024-i-c-s0378426624001043/","summary":"Machine learning methods offer better performance and insights in modeling corporate bond recovery rates than traditional methods.","featured":"2024-06-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":39,"scale":"shares"},{"title":"Information Technology Service Analytics Workload Characterization","url":"/papers/ssrn/4852423/","summary":"Machine learning and big data analytics are being utilized to gain insights from underused IT Ops ticket data.","featured":"2024-06-05","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Symbol Regression","url":"/papers/ssrn/4853200/","summary":"Machine learning is aiding in predicting the adsorption energies of molecules on platinum group metals, vital for catalytic reactions.","featured":"2024-06-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Quality Analytics and Customer Satisfaction: Insights from Retail Industry","url":"/papers/ssrn/4847927/","summary":"Retail industry customer satisfaction can be significantly improved through quality analytics using machine learning algorithms.","featured":"2024-06-05","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"Enhancing Intelligent Search","url":"/papers/ssrn/4847981/","summary":"The article discusses the role of machine learning in intelligent search and information discovery, emphasizing the need for transparent and trustworthy AI models.","featured":"2024-06-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Hands-Free Mouse for Disabled","url":"/papers/ssrn/4850698/","summary":"The article details the creation of a Hands-Free Mouse system for physically disabled individuals, utilizing facial recognition and machine learning techniques.","featured":"2024-06-05","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality","url":"/papers/arxiv/2405.21060/","summary":"The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.","featured":"2024-06-05","label":"Machine learning","topic":"ML & AI Methods","cites":1953,"score":300,"scale":"shares"},{"title":"Improving the Training of Rectified Flows","url":"/papers/arxiv/2405.20320/","summary":"The paper suggests improved methods for training rectified flows in diffusion models for image and video generation, surpassing current distillation methods in low function evaluation settings.","featured":"2024-06-05","label":"Machine learning","topic":"ML & AI Methods","cites":118,"score":49,"scale":"shares"},{"title":"An Organic Weed Control Prototype using Directed Energy and Deep Learning","url":"/papers/arxiv/2405.21056/","summary":"A robot for organic farms uses deep learning to identify and treat weeds with a patented, organic method, achieving up to 98% accuracy.","featured":"2024-06-05","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":6,"scale":"shares"},{"title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","url":"/papers/arxiv/2312.00752/","summary":"Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.","featured":"2024-06-05","label":"Machine learning","topic":"ML & AI Methods","cites":9205,"score":1099,"scale":"shares"},{"title":"DITTO: Diffusion Inference-Time T-Optimization for Music Generation","url":"/papers/arxiv/2401.12179/","summary":"Music Optimization: The DITTO framework allows control of pre-trained text-to-music diffusion models at inference-time, enabling music generation without fine-tuning.","featured":"2024-06-05","label":"Machine learning","topic":"ML & AI Methods","cites":97,"score":252,"scale":"shares"},{"title":"The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence","url":"/papers/arxiv/2403.13784/","summary":"The paper introduces the Model Openness Framework, a system designed to rate machine learning models on their transparency and completeness to promote responsible AI practices.","featured":"2024-06-05","label":"Machine learning","topic":"ML & AI Methods","cites":57,"score":74,"scale":"shares"},{"title":"Machine Learning Loan Prediction","url":"/papers/ssrn/4838474/","summary":"A machine learning model analyzes past loan data to predict the safety of granting loans to individuals, aiming to minimize risk for banks.","featured":"2024-05-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"SubGradient Loss Method","url":"/papers/ssrn/4843397/","summary":"The article introduces a more stable and memory-efficient machine learning method, the Batchstochastic Subgradient method, tested using structured query language.","featured":"2024-05-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"International Patent Office Guidance on Artificial Intelligence Inventions","url":"/papers/ssrn/4843648/","summary":"The article outlines new global rules on the patentability of artificial intelligence inventions issued by major patent offices.","featured":"2024-05-28","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data","url":"/papers/arxiv/2405.15929/","summary":"A proposed semi-supervised deep generative framework integrates consumer preferences and external data into product design, allowing companies to create cost-effective, consumer-preferred designs.","featured":"2024-05-28","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"Reinforcement Learning for Jump-Diffusions","url":"/papers/arxiv/2405.16449/","summary":"The study shows that continuous-time reinforcement learning can be applied to both pure diffusion and jump-diffusion processes, useful in portfolio selection with stock prices modeled as a jump-diffusion.","featured":"2024-05-28","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":5,"scale":"shares"},{"title":"Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis","url":"/papers/arxiv/2405.14868/","summary":"The article introduces GCD, a new technology that can create videos from any perspective without needing depth or 3D scene geometry, with potential applications in robotics and driving environments.","featured":"2024-05-28","label":"Machine learning","topic":"ML & AI Methods","cites":130,"score":79,"scale":"shares"},{"title":"Score-based generative models are provably robust: an uncertainty quantification perspective","url":"/papers/arxiv/2405.15754/","summary":"The authors demonstrate that score-based generative models are resilient to errors, using the Wasserstein uncertainty propagation theorem to explain how learning errors affect the model's quality.","featured":"2024-05-28","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":11,"scale":"shares"},{"title":"NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections","url":"/papers/arxiv/2405.14871/","summary":"The article presents a new ray tracing-based method to enhance Neural Radiance Fields' (NeRFs) rendering of highly reflective objects, showing superior performance and photorealistic results in real-world scenes.","featured":"2024-05-28","label":"Machine learning","topic":"ML & AI Methods","cites":41,"score":10,"scale":"shares"},{"title":"Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations","url":"/papers/arxiv/2405.14857/","summary":"Semantica, an image-conditioned diffusion model, can generate new images based on the semantics of a conditioning image, without the need for fine-tuning.","featured":"2024-05-28","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Reducing Transformer Key-Value Cache Size with Cross-Layer Attention","url":"/papers/arxiv/2405.12981/","summary":"The article introduces Cross-Layer Attention (CLA), a new attention design that minimizes the key-value cache size, allowing for longer sequence lengths and larger batch sizes during inference.","featured":"2024-05-28","label":"Machine learning","topic":"ML & AI Methods","cites":140,"score":206,"scale":"shares"},{"title":"Budgeting Counterfactual for Offline RL","url":"/papers/arxiv/2307.06328/","summary":"The paper suggests a method to improve offline reinforcement learning by limiting out-of-distribution actions during training, showing improved performance on D4RL benchmarks.","featured":"2024-05-28","label":"Machine learning","topic":"ML & AI Methods","cites":6,"score":14,"scale":"shares"},{"title":"Machine Learning Models for Rainfall-Induced Landslide Prediction","url":"/papers/repec/spr-nathaz-v-120-y-2024-i-7-d-10-1007-s11069-024-06405-7/","summary":"The study finds logistic regression models more effective than random forest models in predicting rainfall-induced landslides in India, especially when using antecedent precipitation data.","featured":"2024-05-28","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"RealTime Application Identification Through Network Packet Analysis","url":"/papers/ssrn/4830122/","summary":"The article introduces a new method using AI and machine learning to identify the type of applications used by clients on public internet, improving security and controlling unwanted application usage.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Integrated Text-Model Generation-Simulation Framework for Steel Beam","url":"/papers/ssrn/4831500/","summary":"The paper emphasizes the advantages of automating modelling and simulation processes in research, design, and manufacturing, using machine learning to analyze large-scale simulation data and offer insights into unknown situations.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Business Intelligence through Artificial Intelligence: A Review","url":"/papers/ssrn/4831916/","summary":"A Review: The article reviews the incorporation of AI in business intelligence, focusing on AI-powered data analytics, natural language processing, and big data integration, and how this can improve data visualization and decision-making.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":9,"score":3,"scale":"shares"},{"title":"Leveraging Ai for Effective Human Resource Management: A Comprehensive Overview","url":"/papers/ssrn/4833377/","summary":"The research highlights the benefits and challenges of Artificial Intelligence in Human Resource Management, including recruitment, performance management, skill gaps, and data privacy.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":9,"score":3,"scale":"shares"},{"title":"Sorghum Detection Accuracy","url":"/papers/ssrn/4831588/","summary":"The study introduces an intelligent system for classifying sorghum varieties using machine learning and cloud computing, with the SqueezeNetLR stacking model being the most accurate.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning in Accounting and Finance","url":"/papers/ssrn/4834095/","summary":"Research shows a rising interest in the effects of machine learning on accounting and finance, with a notable increase in studies focusing on Asian markets from 2020-2022.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI Discourse in German and Chinese Media","url":"/papers/ssrn/4834669/","summary":"Analysis of Chinese and German media coverage on AI from 2018 to 2023 shows regional differences, with Chinese media being more positive and German media more critical.","featured":"2024-05-22","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems","url":"/papers/arxiv/2405.11392/","summary":"A proposed deep learning algorithm for optimal stopping problems shows accuracy and efficiency in American option pricing, with its error bound by the loss function and other parameters.","featured":"2024-05-22","label":"arXiv","topic":"ML & AI Methods","cites":7,"score":3,"scale":"shares"},{"title":"Influencer Cartels","url":"/papers/arxiv/2405.10231/","summary":"The article talks about the emergence of 'influencer cartels' in social media marketing, where influencers work together to boost their ad revenue. It also examines how this could affect consumer welfare.","featured":"2024-05-22","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"CAT3D: Create Anything in 3D with Multi-View Diffusion Models","url":"/papers/arxiv/2405.10314/","summary":"Multi-View Diffusion Models: CAT3D is a novel technique for generating 3D scenes from any number of images, surpassing existing methods in speed and efficiency.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI Methods","cites":482,"score":157,"scale":"shares"},{"title":"Octo: An Open-Source Generalist Robot Policy","url":"/papers/arxiv/2405.12213/","summary":"Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI Methods","cites":1880,"score":128,"scale":"shares"},{"title":"Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices","url":"/papers/arxiv/2405.12211/","summary":"Video Editing: Slicedit is a new text-based video editing method that uses a pretrained model to process spatial and spatiotemporal slices, creating videos that maintain the original structure and motion.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI Methods","cites":50,"score":48,"scale":"shares"},{"title":"Aligning Transformers with Continuous Feedback via Energy Rank Alignment","url":"/papers/arxiv/2405.12961/","summary":"The article discusses the energy rank alignment (ERA) algorithm, which effectively generates molecules with specific properties using autoregressive policies.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":25,"scale":"shares"},{"title":"Statistically Truthful Auctions via Acceptance Rule","url":"/papers/arxiv/2405.12016/","summary":"The paper introduces a deep learning approach for ensuring strategy-proofness in auctions, offering statistical guarantees and proving its effectiveness through experiments.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":19,"scale":"shares"},{"title":"Scaling Down Deep Learning with MNIST-1D","url":"/papers/arxiv/2011.14439/","summary":"The article presents MNIST-1D, a cost-effective, low-memory alternative to traditional deep learning benchmarks, designed for efficient study of deep learning structures.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI Methods","cites":35,"score":614,"scale":"shares"},{"title":"On the Efficiency of Convolutional Neural Networks","url":"/papers/arxiv/2404.03617/","summary":"A novel approach to convolutional neural networks prioritizes computational efficiency over arithmetic complexity, resulting in faster, more accurate, and cost-effective models.","featured":"2024-05-22","label":"Machine learning","topic":"ML & AI 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paper suggests using unsupervised machine learning and kmeans clustering to speed up multiscale simulations of heterogeneous quasibrittle materials.","featured":"2024-05-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"ML in Marine Modeling Review","url":"/papers/ssrn/4821241/","summary":"The study analyzes over 200 papers on the use of Machine Learning for managing marine and coastal environments, offering guidance for future research.","featured":"2024-05-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":90,"scale":"shares"},{"title":"Decoding AI Discourse: Analyzing German and Chinese Media (2018-2023) Using Machine Learning Methods","url":"/papers/ssrn/4825371/","summary":"Analysis of Chinese and German media coverage on AI shows regional focus and differing attitudes, with Chinese media being positive and German media being more critical.","featured":"2024-05-15","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"Distilling Diffusion Models into Conditional GANs","url":"/papers/arxiv/2405.05967/","summary":"A new method has been proposed to simplify a complex multistep diffusion model into a single-step conditional GAN model, which speeds up inference and maintains image quality, performing better than other models on the zero-shot COCO benchmark.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":105,"score":160,"scale":"shares"},{"title":"Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers","url":"/papers/arxiv/2405.05945/","summary":"Text Transformation: The Lumina-T2X family, a series of Large Diffusion Transformers, is introduced as a unified framework for transforming noise into various forms of media based on text instructions, allowing for training across different modalities and flexible multimodal data generation.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":146,"score":217,"scale":"shares"},{"title":"Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems","url":"/papers/arxiv/2405.06624/","summary":"The paper introduces guaranteed safe (GS) AI, a set of AI safety approaches that aim to provide AI systems with high-assurance quantitative safety guarantees, achieved through the interaction of a world model, a safety specification, and a verifier, arguing for the necessity of this approach to AI safety.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":139,"score":26,"scale":"shares"},{"title":"Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)","url":"/papers/arxiv/2405.06627/","summary":"The article explores the creation of prediction algorithms for machine learning systems that self-collect data, focusing on managing risk in optimization and active learning tasks.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":27,"score":22,"scale":"shares"},{"title":"AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments","url":"/papers/arxiv/2405.07960/","summary":"AI Evaluation in Clinical Environments: The paper introduces AgentClinic, a benchmark for assessing large language models in simulated clinical environments, highlighting the significant impact of biases on diagnostic accuracy and patient interactions.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":256,"score":21,"scale":"shares"},{"title":"Federated Combinatorial Multi-Agent Multi-Armed Bandits","url":"/papers/arxiv/2405.05950/","summary":"The study presents a federated learning framework for online combinatorial optimization, transforming single-agent algorithms into multi-agent ones, proving efficient in a stochastic data summarization problem.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":14,"score":21,"scale":"shares"},{"title":"The Platonic Representation Hypothesis","url":"/papers/arxiv/2405.07987/","summary":"The authors suggest that AI models, especially deep networks, are moving towards a common statistical model of reality, known as the platonic representation, and discuss its implications and limitations.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI Methods","cites":429,"score":19,"scale":"shares"},{"title":"Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation","url":"/papers/arxiv/2405.05224/","summary":"Emu Diffusion Models: A new distillation framework for diffusion models allows for high-quality sample generation in fewer steps, surpassing current methods in both numerical measurements and human assessments.","featured":"2024-05-15","label":"Machine learning","topic":"ML & AI 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research uses machine learning to predict employee turnover based on various factors, with the Decision Tree model proving most accurate.","featured":"2024-05-15","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Machine Learning for Research Identification","url":"/papers/repec/eee-infome-v-18-y-2024-i-2-s1751157724000300/","summary":"A machine learning framework has been developed to identify significant research papers that initially went unnoticed, proven effective in a chemistry study.","featured":"2024-05-15","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":21,"scale":"shares"},{"title":"Accelerating Tax Statistics Prediction","url":"/papers/repec/zbw-wistat-294178/","summary":"The German Federal Statistical Office is using machine learning to predict pension taxation data, aiming for quicker statistics publication.","featured":"2024-05-15","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":12,"scale":"shares"},{"title":"Machine Learning for Design Recognition","url":"/papers/ssrn/4818941/","summary":"The article explores how machine learning can identify design patterns in Object-Oriented Programming, potentially simplifying software maintenance.","featured":"2024-05-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Hierarchical graph-based machine learning model for optimization of three- dimensional braced steel frame","url":"/papers/ssrn/4815849/","summary":"The paper suggests a machine learning method for optimizing the structure of 3D frames under seismic loads, surpassing traditional methods.","featured":"2024-05-08","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Permutation-Invariant NN Analysis","url":"/papers/ssrn/4814691/","summary":"The study explores permutation-invariant neural networks, which can process various data formats and generate results unaffected by the input data's sequence.","featured":"2024-05-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Explainable Autoencoder Anomaly Detection","url":"/papers/ssrn/4819146/","summary":"The suggested model uses an explainable variational autoencoder to detect anomalies in multivariate time series data, overcoming issues of large data size, unknown anomalies, and unclear deep learning detection methods.","featured":"2024-05-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Big Data and Machine Learning","url":"/papers/ssrn/4815936/","summary":"The combination of big data and machine learning in the defence sector improves intelligence, strategic decision-making, and operational efficiency, but also brings up issues about data privacy, ethical implications, and potential misuse.","featured":"2024-05-08","label":"SSRN","topic":"ML & AI 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tasks. This algorithm merges model-based and model-free reinforcement learning methods, and optimizes regret by balancing exploration and exploitation costs. It is expected to achieve a regret of order √T over T trials.","featured":"2024-05-08","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":3,"scale":"shares"},{"title":"FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials","url":"/papers/arxiv/2405.01491/","summary":"FeNNol is a new library for building, training, and running force-field-enhanced neural network potentials, providing a flexible and modular system for building hybrid models and fast potential evaluation.","featured":"2024-05-08","label":"Machine learning","topic":"ML & AI Methods","cites":21,"score":31,"scale":"shares"},{"title":"Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks","url":"/papers/arxiv/2405.02225/","summary":"The paper presents a framework for post-processing machine learning models to ensure multi-group fairness in predictions, applicable in image segmentation, hierarchical classification, and text generation.","featured":"2024-05-08","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":12,"scale":"shares"},{"title":"Capabilities of Gemini Models in Medicine","url":"/papers/arxiv/2404.18416/","summary":"Med-Gemini, an AI model for medical applications, outperforms previous models and human experts in medical benchmarks, indicating potential for real-world medical use.","featured":"2024-05-08","label":"Machine learning","topic":"ML & AI Methods","cites":446,"score":1114,"scale":"shares"},{"title":"Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA","url":"/papers/arxiv/2304.06027/","summary":"CLoRA, a new method, prevents catastrophic forgetting in text-to-image models when introducing new concepts, achieving top performance in continual learning settings for image classification.","featured":"2024-05-08","label":"Machine learning","topic":"ML & AI Methods","cites":175,"score":192,"scale":"shares"},{"title":"Fourier Neural Operator with Learned Deformations for PDEs on General Geometries","url":"/papers/arxiv/2207.05209/","summary":"The study introduces geo-FNO, a new framework for solving partial differential equations on any geometry, proving to be faster and more accurate than standard and machine learning-based solvers.","featured":"2024-05-08","label":"Machine learning","topic":"ML & AI Methods","cites":727,"score":126,"scale":"shares"},{"title":"Intelligent Control Algorithms for Seismic Reliability","url":"/papers/ssrn/4807879/","summary":"A new method using probability density evolution and a machine learning algorithm has been proposed to assess the seismic reliability of aqueduct structures, showing improved reliability against random seismic effects.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Online Machine Learning for Bandgap Engineering","url":"/papers/ssrn/4808029/","summary":"MatFlow, a web-based machine learning platform for materials science, supports both forward and inverse design of materials, with a specific application in bandgap prediction.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Seismic Design for Girder Bridges","url":"/papers/ssrn/4807875/","summary":"A study suggests a reliable method for designing a rational seismic system for small and medium-span girder bridges using a real bridge database and machine learning algorithms.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Pruning CNNs for Prediction","url":"/papers/ssrn/4810047/","summary":"A study investigates the uncertainty of predictions from pruned neural network models in the context of conformal prediction, with a focus on filter-level pruning in convolutional neural networks.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Ensemble Methods for Learning","url":"/papers/ssrn/4807694/","summary":"The efficiency of semi-supervised learning in machine learning can be enhanced by using methods that prevent overfitting and eliminate the need for incremental labeling, such as implementing diversity strategies.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"UHPFRC Shear-Critical Beams with ML","url":"/papers/ssrn/4807675/","summary":"A new method using machine learning has been created to analyze ultra-high-performance fiber-reinforced concrete.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"On the Use of Artificial Intelligence in the Financial Services Industry and its Potential Risks","url":"/papers/ssrn/4811629/","summary":"Risks vs Rewards: The article highlights the risks of using AI in finance due to a lack of understanding of its technical aspects.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses","url":"/papers/ssrn/4806426/","summary":"AI is being used to predict the success of crowdfunding campaigns based on textual descriptions.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":4,"score":3,"scale":"shares"},{"title":"Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements","url":"/papers/ssrn/4813310/","summary":"The article introduces a deep-learning AI model that surpasses human analysts in predicting stock prices a year ahead, using large data sets efficiently.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":3,"scale":"shares"},{"title":"Risks of Generative AI in Finance","url":"/papers/ssrn/4811811/","summary":"The article addresses the ethical, societal, and security concerns of generative AI systems in finance, advocating for thorough testing, strong security, and regulatory involvement.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning in Stock Returns","url":"/papers/ssrn/4811748/","summary":"A novel method has been created to predict stock returns, solving the low signal-to-noise ratio problem and applicable to all US stocks by linking characteristics and stock returns.","featured":"2024-05-01","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Merchants of Vulnerabilities: How Bug Bounty Programs Benefit Software Vendors","url":"/papers/arxiv/2404.17497/","summary":"The paper highlights the strategic advantages of bug bounty programs for software vendors, demonstrating that they can boost profits, expedite software launches, and improve security by encouraging ethical hackers to identify and report vulnerabilities.","featured":"2024-05-01","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":2,"scale":"shares"},{"title":"Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds","url":"/papers/arxiv/2404.16169/","summary":"A model is created to predict potential targets of activist investment funds, using Russell 3000 index data and identifying key factors through the Shapley value method.","featured":"2024-05-01","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Learning general Gaussian mixtures with efficient score matching","url":"/papers/arxiv/2404.18893/","summary":"The research introduces an algorithm for learning mixtures of Gaussians in multiple dimensions using diffusion models, marking a significant advancement in unsupervised learning tasks.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":44,"score":14,"scale":"shares"},{"title":"Learning Visuotactile Skills With Two Multifingered Hands","url":"/papers/arxiv/2404.16823/","summary":"The paper presents a study on learning from human demonstrations using a bimanual system with multifingered hands, introducing a low-cost teleoperation system and a novel hardware adaptation using two prosthetic hands with touch sensors.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":159,"score":10,"scale":"shares"},{"title":"DPO Meets PPO: Reinforced Token Optimization for RLHF","url":"/papers/arxiv/2404.18922/","summary":"A new framework is introduced that models Reinforcement Learning from Human Feedback as a Markov decision process, using an algorithm that learns from preference data.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":143,"score":9,"scale":"shares"},{"title":"Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields","url":"/papers/arxiv/2404.17528/","summary":"The paper introduces a framework that improves the synthesis of novel views for unseen scenes, using strategies like Adaptive Cost Aggregation, Spatial-View Aggregator, and Consistency-Aware Fusion.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":5,"scale":"shares"},{"title":"Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models","url":"/papers/arxiv/2310.13828/","summary":"Poisoning Attacks on Text-to-Image Models: The article introduces Nightshade, an attack on text-to-image models that can disrupt their functionality, potentially serving as a defense against web scrapers.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":141,"score":3299,"scale":"shares"},{"title":"Learning Performance-Improving Code Edits","url":"/papers/arxiv/2302.07867/","summary":"The research presents a framework for optimizing programs using large language models, achieving a mean speedup of 6.86, outperforming average individual programmers.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":164,"score":671,"scale":"shares"},{"title":"Bagging Provides Assumption-free Stability","url":"/papers/arxiv/2301.12600/","summary":"The paper offers a guarantee on the stability of bagging for any machine learning model, regardless of data distribution or algorithm properties, supported by empirical results.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":31,"score":21,"scale":"shares"},{"title":"Overload: Latency Attacks on Object Detection for Edge Devices","url":"/papers/arxiv/2304.05370/","summary":"The study explores latency attacks on deep learning applications, specifically object detection, and introduces a framework, Overload, that can generate such attacks, posing a threat to systems with limited resources.","featured":"2024-05-01","label":"Machine learning","topic":"ML & AI Methods","cites":31,"score":20,"scale":"shares"},{"title":"Byzantine-Robust Federated Learning with Clustering Model Updates","url":"/papers/ssrn/4801966/","summary":"The paper presents FedCmp, a method to protect federated learning from Byzantine attacks by detecting malicious updates using a multiround voting system.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Innovation Strategy After IPO: How AI Analytics Spurs Innovation After IPO","url":"/papers/ssrn/4803528/","summary":"Companies that adopt AI analytics after their IPOs experience a smaller drop in innovation quality, as AI analytics helps alleviate the pressure to meet short-term financial targets and disclosure obligations.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":34,"score":2,"scale":"shares"},{"title":"Has Machine Learning Defeated Trend Following Strategies in the Chinese Futures Market","url":"/papers/ssrn/4802493/","summary":"Machine learning models, especially Multilayer Perceptron (MLP), excel in predicting returns in the Chinese commodity futures market, due to their ability to identify complex patterns and use both volume and price data.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Enhancing Crop Yield Prediction and Management in eFarming Systems through Machine Learning","url":"/papers/ssrn/4803868/","summary":"The study uses machine learning to improve crop yield predictions in eFarming, enhancing forecast accuracy and reliability.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"Investigating on the Recent Synergies of IoT and Machine Learning for Proactive Healthcare Systems","url":"/papers/ssrn/4798953/","summary":"The article explores the role of IoT and Machine Learning in transforming healthcare management, enabling early disease detection and improved patient care.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Recent Improvements in Cloud Resource Optimization with Dynamic Workloads using Machine Learning","url":"/papers/ssrn/4803863/","summary":"The article examines the use of machine learning in optimizing cloud resources for fluctuating workloads, discussing the pros and cons of current techniques.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"A Survey on IoT Device Authentication and Anomaly Detection for Cyber Security using Machine Learning","url":"/papers/ssrn/4798899/","summary":"The study explores the application of machine learning in boosting cybersecurity through IoT device authentication and anomaly detection.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":3,"scale":"shares"},{"title":"AI Expert Testimony Admissibility","url":"/papers/ssrn/4798356/","summary":"The use of AI-based evidence in court depends on the transparency of the AI system's methodology and the court's ability to evaluate it.","featured":"2024-04-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty","url":"/papers/arxiv/2404.12598/","summary":"The article discusses continuous-time risk-sensitive reinforcement learning. It shows its similarity to maintaining the martingale property of a process involving the value function and the q-function. The paper also suggests an algorithm that includes risk sensitivity and proves its effectiveness for Merton's investment problem and its enhanced performance in the linear-quadratic control problem.","featured":"2024-04-24","label":"arXiv","topic":"ML & AI Methods","cites":12,"score":3,"scale":"shares"},{"title":"Score matching for sub-Riemannian bridge sampling","url":"/papers/arxiv/2404.15258/","summary":"The study demonstrates a method for bridge simulation on sub-Riemannian manifolds, showing how machine learning can be adapted for training on these manifolds.","featured":"2024-04-24","label":"Machine learning","topic":"ML & AI Methods","cites":5,"score":21,"scale":"shares"},{"title":"Mastering Diverse Domains through World Models","url":"/papers/arxiv/2301.04104/","summary":"Algorithm Mastery: DreamerV3, a universal algorithm, excels in over 150 varied tasks, including diamond collection in Minecraft without human input, expanding the scope of reinforcement learning.","featured":"2024-04-24","label":"Machine learning","topic":"ML & AI Methods","cites":1418,"score":3254,"scale":"shares"},{"title":"AI Consciousness is Inevitable: A Theoretical Computer Science Perspective","url":"/papers/arxiv/2403.17101/","summary":"A machine model for consciousness, influenced by Alan Turing's computation model and Bernard Baars' theater model, aligns with major theories of human and animal consciousness, indicating the inevitability of machine consciousness.","featured":"2024-04-24","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":179,"scale":"shares"},{"title":"A predictive machine learning force field framework for liquid electrolyte development","url":"/papers/arxiv/2404.07181/","summary":"MLFF Electrolyte Development Framework: The paper introduces BAMBOO, a new framework for molecular dynamics simulations, effective in predicting properties of liquid electrolytes for lithium batteries.","featured":"2024-04-24","label":"Machine learning","topic":"ML & AI Methods","cites":36,"score":39,"scale":"shares"},{"title":"Trends, Applications, and Challenges in Human Attention Modelling","url":"/papers/arxiv/2402.18673/","summary":"The survey reviews recent attempts to incorporate human attention mechanisms into deep learning models, discussing future research areas and challenges.","featured":"2024-04-24","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":37,"scale":"shares"},{"title":"Machine Learning vs. Logistic Regression","url":"/papers/repec/ids-ijmefi-v-17-y-2024-i-1-p-29-48/","summary":"Machine learning models, particularly XGBoost, outperform logistic regressions in predicting credit risk in Brazilian wholesale firms, according to a study.","featured":"2024-04-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":30,"scale":"shares"},{"title":"Addressing sample bias in ML","url":"/papers/repec/wly-japmet-v-39-y-2024-i-3-p-383-400/","summary":"The research suggests two control function methods to improve machine learning accuracy when training and prediction samples differ, reducing prediction error and selection bias.","featured":"2024-04-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Intelligent beneficiary selection for social programs","url":"/papers/repec/ids-injdan-v-16-y-2024-i-2-p-181-206/","summary":"A prediction model using the random forest-based machine learning algorithm can accurately identify beneficiaries for social safety programs, improving traditional manual systems.","featured":"2024-04-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Software estimation with statistical models","url":"/papers/repec/gam-jmathe-v-12-y-2024-i-7-p-1058-d-1368511/","summary":"Machine learning techniques, particularly Random Forest, can accurately predict software development time and effort, reducing the risk of miscalculations.","featured":"2024-04-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Data Augmentation for Legal Cases","url":"/papers/repec/spr-trosos-v-18-y-2024-i-1-d-10-1007-s12626-024-00158-2/","summary":"The article explores the application of machine learning in the COLIEE competition, using data augmentation to enhance the analysis of legal documents.","featured":"2024-04-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"River Sediment Load Prediction with ML","url":"/papers/repec/tec-techni-v-4-y-2022-i-1-p-239-249/","summary":"The research concludes that the Multilayer perceptron algorithm is the most effective for predicting suspended sediment load, based on machine learning studies.","featured":"2024-04-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Machine Learning in Fund Classification","url":"/papers/ssrn/4794900/","summary":"The paper uses machine learning to categorize hedge funds, finding that those classified as systematic yield higher excess returns.","featured":"2024-04-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Enhancing path-integral approximation for non-linear diffusion with neural network","url":"/papers/arxiv/2404.08903/","summary":"The paper improves the pricing of fixed income instruments within the Black-Karasinski model using neural networks, showing better results for multiple calibrations over extended periods.","featured":"2024-04-17","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations","url":"/papers/arxiv/2404.08456/","summary":"The study introduces a new deep learning algorithm for solving complex backward stochastic differential equations, proving its effectiveness with numerous numerical tests.","featured":"2024-04-17","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":6,"scale":"shares"},{"title":"Dataset Reset Policy Optimization for RLHF","url":"/papers/arxiv/2404.08495/","summary":"The DR-PO algorithm enhances Reinforcement Learning by incorporating offline preference data into online policy training, outperforming other techniques in summarization and the Anthropic Helpful Harmful dataset.","featured":"2024-04-17","label":"Machine learning","topic":"ML & AI Methods","cites":44,"score":23,"scale":"shares"},{"title":"An Empirical Study of $\\mu$P Learning Rate Transfer","url":"/papers/arxiv/2404.05728/","summary":"Neural Network Scaling Rules: A study has found that the μ-Parameterization (μP) is generally effective in determining the best learning rates for large neural network models, although it doesn't work in all situations.","featured":"2024-04-17","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":109,"scale":"shares"},{"title":"A Dynamical Model of Neural Scaling Laws","url":"/papers/arxiv/2402.01092/","summary":"A study examines a random feature model trained with gradient descent, providing insights into neural scaling laws, including the correlation between performance, training time, model size, and the increasing gap between training and test loss due to repeated data use.","featured":"2024-04-17","label":"Machine learning","topic":"ML & AI Methods","cites":112,"score":69,"scale":"shares"},{"title":"AI in Finance: Data and Opportunities","url":"/papers/repec/eee-pacfin-v-84-y-2024-i-c-s0927538x24000581/","summary":"Data and Opportunities: The article highlights the significant role of big data and AI in the finance industry, suggesting a blend of financial knowledge and data analytics for improved financial systems.","featured":"2024-04-17","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":26,"scale":"shares"},{"title":"Why ‘machine Learning’ is a Misnomer","url":"/papers/ssrn/4784181/","summary":"The article critiques the term 'machine learning', arguing it doesn't accurately represent machine algorithms and suggests a reevaluation of the narratives around these technologies.","featured":"2024-04-10","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":18,"scale":"shares"},{"title":"AI Consensus on Pricing","url":"/papers/ssrn/4786984/","summary":"The article discusses the disagreement among machine learning models about which factors affect returns, suggesting that a unified model is impossible with current data.","featured":"2024-04-10","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism","url":"/papers/arxiv/2404.04245/","summary":"The study investigates adversarial attacks on Deep Neural Networks for image classification, highlighting the Fast Gradient Sign Method and the Carlini-Wagner approach, and suggests defensive distillation as a defense, effective against FGSM but vulnerable to CW attacks.","featured":"2024-04-10","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":7,"scale":"shares"},{"title":"Data Sensitivity in Machine Learning Reuse","url":"/papers/repec/spr-infosf-v-26-y-2024-i-2-d-10-1007-s10796-023-10388-4/","summary":"The article explores the difficulties in reusing machine learning applications due to data sensitivity and domain specificity, categorizing applications into four types based on reuse strategies.","featured":"2024-04-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":23,"scale":"shares"},{"title":"CostSensitive Machine Learning for Investments","url":"/papers/repec/wly-isacfm-v-31-y-2024-i-1-n-e1548/","summary":"The study employs cost-sensitive machine learning models to predict startup success, potentially reducing investor risk but possibly limiting gains, and proposes ways to improve successful startup detection.","featured":"2024-04-10","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":22,"scale":"shares"},{"title":"Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior","url":"/papers/arxiv/2403.19781/","summary":"The research introduces a market simulation framework using reinforcement learning agents that can mimic real-world market dynamics and adapt to major market events.","featured":"2024-04-03","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":2,"scale":"shares"},{"title":"Using Images as Covariates: Measuring Curb Appeal with Deep Learning","url":"/papers/arxiv/2403.19915/","summary":"Incorporating image data into econometric models through deep learning enhances the accuracy of residential real estate price predictions.","featured":"2024-04-03","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Aggregating Autoencoders for Persistent Access Threats","url":"/papers/ssrn/4781054/","summary":"The article presents AEAPT, a deep learning method for detecting and isolating long-term, undetected cyberattacks.","featured":"2024-04-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":6,"scale":"shares"},{"title":"Predicting Beta","url":"/papers/ssrn/4778123/","summary":"Machine Learning algorithms enhance the precision of estimating equity betas for private or nontraded assets, particularly for smaller, younger firms with unique capital structures.","featured":"2024-04-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS","url":"/papers/arxiv/2311.17245/","summary":"LightGaussian is a new method that converts 3D Gaussians into a more compact format, enhancing efficiency in real-time neural rendering and reducing storage needs.","featured":"2024-04-03","label":"Machine learning","topic":"ML & AI Methods","cites":660,"score":519,"scale":"shares"},{"title":"Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion","url":"/papers/arxiv/2308.12469/","summary":"A new method using self-attention layers in stable diffusion models achieves superior zero-shot segmentation without annotations, outperforming previous methods on the COCO-Stuff-27 dataset.","featured":"2024-04-03","label":"Machine learning","topic":"ML & AI Methods","cites":171,"score":107,"scale":"shares"},{"title":"Game AI Evolution","url":"/papers/ssrn/4772907/","summary":"The research investigates the use of deep reinforcement learning in creating adaptable and compliant gaming AI, overcoming limitations of traditional methods.","featured":"2024-03-27","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Machine Learning Guided Proof of Beal's Conjecture","url":"/papers/ssrn/4770977/","summary":"The paper provides a proof of Beals conjecture in number theory using machine learning, highlighting its potential in discovering mathematical proofs.","featured":"2024-03-27","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Two Layers Are All You Need","url":"/papers/ssrn/4769370/","summary":"The article explores the paradox of larger deep neural networks performing better than smaller ones, despite theories suggesting one layer should suffice.","featured":"2024-03-27","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Robust Utility Optimization via a GAN Approach","url":"/papers/arxiv/2403.15243/","summary":"The study uses a generative adversarial network (GAN) to solve utility optimization problems in market settings, performing as well as optimal strategies and better in settings without a known optimal strategy.","featured":"2024-03-27","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":5,"scale":"shares"},{"title":"Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science","url":"/papers/arxiv/2303.00652/","summary":"The paper evaluates different explainable artificial intelligence methods in the context of climate science, comparing their suitability for specific research problems.","featured":"2024-03-27","label":"Machine learning","topic":"ML & AI Methods","cites":90,"score":53,"scale":"shares"},{"title":"Machine Learning Workflow for China's Financial Crisis","url":"/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-023-00574-3/","summary":"Researchers have developed a multistep workflow using machine learning to predict China's systemic financial crises, successfully identifying six high-risk periods from 1990 to 2020.","featured":"2024-03-20","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Variogram Inference with CNNs","url":"/papers/ssrn/4753122/","summary":"The paper introduces a new method for deducing covariance functions from sparse data using Convolutional Neural Networks, offering high accuracy and low computational time.","featured":"2024-03-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Data Preprocessing for Code Smell Detection","url":"/papers/ssrn/4756315/","summary":"The review examines 69 studies on machine learning's role in detecting code smell (error detection), emphasizing the importance of data preprocessing techniques.","featured":"2024-03-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI in Finance: Opportunities","url":"/papers/ssrn/4753640/","summary":"Opportunities: Big data and AI are transforming finance and accounting sectors by altering data processing and decision-making, with increased use of machine learning and AI analytics for empirical evidence analysis.","featured":"2024-03-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Artificial Intelligence, Data and Competition","url":"/papers/arxiv/2403.06150/","summary":"The first article investigates how companies' algorithm-based pricing strategies can result in collusion and price discrimination, negatively impacting consumer surplus and societal welfare.","featured":"2024-03-13","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":2,"scale":"shares"},{"title":"GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks","url":"/papers/arxiv/2403.04747/","summary":"A new variance-preserving aggregation function for Graph Neural Networks is proposed, which could lead to more efficient and self-normalizing networks.","featured":"2024-03-13","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":77,"scale":"shares"},{"title":"BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization","url":"/papers/arxiv/2403.04763/","summary":"Graph Machine Learning via Bilevel Optimization: The paper views graph learning techniques as special cases of bilevel optimization, introducing a new class of energy functions for graph neural network layers, and showcasing the versatility of this approach through empirical results.","featured":"2024-03-13","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":12,"scale":"shares"},{"title":"Settling the Sample Complexity of Model-Based Offline Reinforcement Learning","url":"/papers/arxiv/2204.05275/","summary":"A paper reveals that a model-based approach can achieve optimal sample complexity without burn-in cost in offline reinforcement learning for tabular Markov decision processes, providing an efficient solution for sample-starved applications.","featured":"2024-03-13","label":"Machine learning","topic":"ML & AI Methods","cites":113,"score":31,"scale":"shares"},{"title":"Transformer for Times Series: an Application to the S&P500","url":"/papers/arxiv/2403.02523/","summary":"The research investigates the use of transformer models in financial time series prediction, showing promising results with synthetic data and insightful findings on S&P500 data volatility prediction.","featured":"2024-03-06","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":3,"scale":"shares"},{"title":"AI Risk Package","url":"/papers/ssrn/4744576/","summary":"The authors introduce a Python package and metrics for managing risks in Artificial Intelligence applications, emphasizing their interpretability and reproducibility.","featured":"2024-03-06","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"A Yield-Based Asset Ratio to Boost Minimum Investment Returns","url":"/papers/ssrn/4746302/","summary":"The article proposes a strategy to improve weak medium-term returns in retirement portfolios by adjusting the stock percentage based on the earnings yield of stock and the current yield of bonds, with caution needed when stock prices exceed sustainable levels.","featured":"2024-03-06","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Active Statistical Inference","url":"/papers/arxiv/2403.03208/","summary":"A new method called Active inference, which uses machine learning to collect data and focuses on areas where the model is uncertain, achieves the same accuracy with fewer samples than previous methods.","featured":"2024-03-06","label":"Machine learning","topic":"ML & AI Methods","cites":53,"score":32,"scale":"shares"},{"title":"Behavior Generation with Latent Actions","url":"/papers/arxiv/2403.03181/","summary":"The Vector-Quantized Behavior Transformer (VQ-BeT), a new model for behavior generation, improves multimodal action prediction, conditional generation, and partial observations, and speeds up inference.","featured":"2024-03-06","label":"Machine learning","topic":"ML & AI Methods","cites":221,"score":32,"scale":"shares"},{"title":"Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking","url":"/papers/arxiv/2403.03185/","summary":"A novel method to prevent reward hacking in AI systems uses state occupancy measure instead of action distribution, effectively avoiding significant drops in true reward.","featured":"2024-03-06","label":"Machine learning","topic":"ML & AI Methods","cites":55,"score":10,"scale":"shares"},{"title":"AI Assets Connectedness with Traditional Classes","url":"/papers/repec/eee-intfin-v-91-y-2024-i-c-s104244312300197x/","summary":"Research suggests AI tokens can diversify traditional assets under normal market conditions, but fail to do so during extreme market shocks.","featured":"2024-03-06","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"AI for human learning","url":"/papers/repec/onl-ajoeal-v-9-y-2024-i-1-p-1-21-id-1024/","summary":"The paper presents a framework for integrating AI into education and proposes a learning design model for AI-based learning support systems.","featured":"2024-03-06","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":12,"scale":"shares"},{"title":"Machine learning in supply chain","url":"/papers/repec/ids-ijlsma-v-47-y-2024-i-3-p-327-355/","summary":"The research reviews the use of machine learning in supply chain management, providing insights for future studies in this area.","featured":"2024-03-06","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":12,"scale":"shares"},{"title":"Independent Directors","url":"/papers/repec/spr-jahrfr-v-43-y-2023-i-3-d-10-1007-s10037-023-00198-1/","summary":"The research uses micro-scale job-household data and machine learning to analyze spatiotemporal patterns in Tokyo, highlighting urbanization and suburbanization trends.","featured":"2024-03-06","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"Jump Components in Jump Diffusion Models","url":"/papers/ssrn/4732803/","summary":"A proposed nonparametric test can determine if a jump diffusion process contains a jump component or is a diffusion, with the test statistic showing standard normal distribution if there are no jumps.","featured":"2024-02-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction","url":"/papers/arxiv/2402.10760/","summary":"Risk-Aware Stock Prediction: The RAGIC model, using a Generative Adversarial Network, accurately predicts future stock prices with a consistent 95% coverage.","featured":"2024-02-21","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"Identifying regions of importance in wall-bounded turbulence through explainable deep learning","url":"/papers/arxiv/2302.01250/","summary":"A deep-learning method has been used to analyze energy structures in turbulence, finding that the most significant structures aren't always the ones contributing most to Reynolds shear stress.","featured":"2024-02-21","label":"Machine learning","topic":"ML & AI Methods","cites":88,"score":94,"scale":"shares"},{"title":"Graph Mamba: Towards Learning on Graphs with State Space Models","url":"/papers/arxiv/2402.08678/","summary":"Graph Mamba Networks, a new type of Graph Neural Networks, have been introduced, which achieve excellent performance in various benchmark datasets despite lower computational cost.","featured":"2024-02-21","label":"Machine learning","topic":"ML & AI Methods","cites":167,"score":39,"scale":"shares"},{"title":"Financial Machine Learning with R","url":"/papers/repec/pra-mprapa-119998/","summary":"The paper critiques the use of machine learning in finance, offering guidance on method selection and referencing R libraries for computation.","featured":"2024-02-21","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Asset Embeddings","url":"/papers/ssrn/4723350/","summary":"The paper suggests that investors' holdings data, when analyzed with artificial intelligence and machine learning, can reveal significant company traits.","featured":"2024-02-14","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Market Model Calibration via Neural Network","url":"/papers/ssrn/4724549/","summary":"Faster Efficiency: Machine learning can streamline the calibration of market models, reducing computational time and increasing practicality, with a new approach allowing model parameters to evolve as a stochastic process for a robust training set.","featured":"2024-02-14","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices","url":"/papers/arxiv/2303.10019/","summary":"A new method for merging multivariate probabilistic forecasts, considering dependencies between quantiles and marginals, has shown significant improvements in predicting day-ahead electricity prices.","featured":"2024-02-14","label":"arXiv","topic":"ML & AI Methods","cites":24,"score":17,"scale":"shares"},{"title":"Scaling Laws for Fine-Grained Mixture of Experts","url":"/papers/arxiv/2402.07871/","summary":"The research introduces a new hyperparameter, granularity, to Mixture of Experts models, improving training optimization and outperforming dense Transformers.","featured":"2024-02-14","label":"Machine learning","topic":"ML & AI Methods","cites":181,"score":120,"scale":"shares"},{"title":"Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning","url":"/papers/arxiv/2402.00787/","summary":"A new method is suggested for depicting diverse processing-limited agents in a multi-agent reinforcement learning system, showing enhanced predictive ability in multiple real-world situations.","featured":"2024-02-07","label":"arXiv","topic":"ML & AI Methods","cites":7,"score":5,"scale":"shares"},{"title":"Financial Applications of Machine Learning Using R Software","url":"/papers/ssrn/4716425/","summary":"The article discusses various Machine Learning techniques used in finance and offers advice on selecting methods for financial applications.","featured":"2024-02-07","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":5,"scale":"shares"},{"title":"Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks","url":"/papers/ssrn/4712741/","summary":"The study shows that using daily satellite images and machine learning to track economic activity is more effective than traditional models, particularly in the cement and construction industries.","featured":"2024-02-07","label":"SSRN","topic":"ML & AI Methods","cites":5,"score":3,"scale":"shares"},{"title":"Cyber Risk and Stock Returns","url":"/papers/ssrn/4716975/","summary":"A machine learning algorithm that measures a firm's proximity to cyber risk outperforms traditional methods, with stocks at high cyber risk generating significant additional returns.","featured":"2024-02-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Dimensionality Reduction with Dynamics & ML","url":"/papers/repec/eee-matcom-v-218-y-2024-i-c-p-98-111/","summary":"A new method that merges dynamical mechanisms and machine learning has been developed to simplify high-dimensional complex systems, demonstrating strong predictive capabilities even with noisy data.","featured":"2024-02-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":19,"scale":"shares"},{"title":"Gold Price Prediction with Hurst-based ML","url":"/papers/repec/eee-jrpoli-v-88-y-2024-i-c-s0301420723011418/","summary":"A new hybrid model using machine learning and Hurst-oriented reconfiguration has been developed to predict gold prices more accurately than traditional models.","featured":"2024-02-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Machine Learning Simplifies Finance in Real Time","url":"/papers/repec/ces-ceswps-10909/","summary":"The article introduces a new deep learning algorithm designed to solve complex financial models. This algorithm provides new economic insights while keeping computational costs low.","featured":"2024-02-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":11,"scale":"shares"},{"title":"Machine Learning for Landslide Susceptibility in Doboj City","url":"/papers/repec/taf-tjomxx-v-19-y-2023-i-1-p-2163199/","summary":"A machine learning model was developed to assess landslide risks in Doboj City, achieving a 92% accuracy rate.","featured":"2024-02-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"MTRGL: Effective Temporal Correlation Discerning Through Multi-Modal Temporal Relational Graph Learning","url":"/papers/arxiv/2401.14199/","summary":"Temporal Correlation Discerning: The paper introduces a new framework, Multi-modal Temporal Relation Graph Learning (MTRGL), that merges time series data and discrete features to improve automated pair trading strategies.","featured":"2024-01-30","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":15,"scale":"shares"},{"title":"Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru","url":"/papers/arxiv/2401.13977/","summary":"The research examines mode choice decision making behavior using a Multinomial logit model and machine learning classifiers, and employs modern interpretability techniques to explain the decision making behavior.","featured":"2024-01-30","label":"arXiv","topic":"ML & AI Methods","cites":5,"score":5,"scale":"shares"},{"title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","url":"/papers/arxiv/2401.14404/","summary":"The study investigates Denoising Diffusion Models (DDM) and their ability to learn representations, suggesting a simplified approach similar to a Denoising Autoencoder (DAE).","featured":"2024-01-30","label":"Machine learning","topic":"ML & AI Methods","cites":126,"score":114,"scale":"shares"},{"title":"Rethinking Patch Dependence for Masked Autoencoders","url":"/papers/arxiv/2401.14391/","summary":"The research proposes a new pretraining framework, Cross-Attention Masked Autoencoders (CrossMAE), which performs as well as Masked Autoencoders (MAE) but with less decoding computation.","featured":"2024-01-30","label":"Machine learning","topic":"ML & AI Methods","cites":47,"score":98,"scale":"shares"},{"title":"Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint","url":"/papers/arxiv/2401.13624/","summary":"The study offers a theoretical insight into the robust overfitting issue in adversarial training on over-parameterized deep neural networks (DNNs), showing that overfitting can be avoided and a robust generalization gap is unavoidable, with the model capacity requirement depending on the target function's smoothness.","featured":"2024-01-30","label":"Machine learning","topic":"ML & AI Methods","cites":2,"score":19,"scale":"shares"},{"title":"Active Learning for Ensemble Models","url":"/papers/repec/gam-jstats-v-7-y-2024-i-1-p-8-137-d-1325699/","summary":"Active learning within ensemble learning can achieve similar predictive performance on a limited budget, with boosting or stacking models outperforming the SVM model when using the same uncertainty sampling.","featured":"2024-01-30","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":11,"scale":"shares"},{"title":"Guidelines for Double/Debiased ML in Economics","url":"/papers/ssrn/4703243/","summary":"The article discusses the integration of machine learning in economics, focusing on the DoubleDebiased Machine Learning framework and the importance of model generalizability.","featured":"2024-01-23","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":4,"scale":"shares"},{"title":"AI Thrust: Ranking Emerging Powers for Tech Startup Investment in Latin America","url":"/papers/arxiv/2401.09056/","summary":"The paper ranks Latin American countries on their potential to emerge as AI powers, with Argentina, Colombia, Uruguay, Costa Rica, and Ecuador leading.","featured":"2024-01-23","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":5,"scale":"shares"},{"title":"Flame: Simplifying Topology Extension in Federated Learning","url":"/papers/arxiv/2305.05118/","summary":"Flame is a novel system for distributed machine learning that provides flexibility in setting up federated learning applications, separates application logic from deployment specifics, and supports various topologies and mechanisms.","featured":"2024-01-23","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":19,"scale":"shares"},{"title":"ML Models' Predictability of Commodity Futures Returns","url":"/papers/repec/wly-jfutmk-v-44-y-2024-i-2-p-302-322/","summary":"Light gradient-boosting machine learning models outperform linear models in predicting future returns in 22 commodities.","featured":"2024-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Incorporating ESG Ratings for Profitable Investments","url":"/papers/repec/taf-jsustf-v-14-y-2024-i-1-p-184-198/","summary":"Companies with high environmental, social, and corporate governance scores are financially more successful, with machine learning predicting a 14% higher return on equity.","featured":"2024-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"PP Lending Platform Failure Prediction in China","url":"/papers/repec/eee-finlet-v-59-y-2024-i-c-s154461232301156x/","summary":"Machine learning is used in a study to accurately predict the failure of P2P lending platforms in China by identifying key variables.","featured":"2024-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Chaos Theory in Economics","url":"/papers/repec/gam-jmathe-v-12-y-2023-i-1-p-92-d-1308393/","summary":"The article discusses the growth of chaos mathematics, its applications in fields like topology and Catastrophe Theory, and the potential of Quantum Algorithms and AI to improve predictions in chaotic systems, especially in economics.","featured":"2024-01-23","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Model Averaging and Double Machine Learning","url":"/papers/ssrn/4691169/","summary":"The article presents two new stacking methods for double-debiased machine learning (DDML), showing its robustness against unknown functional forms, with software available in Stata and R.","featured":"2024-01-17","label":"SSRN","topic":"ML & AI Methods","cites":25,"score":3,"scale":"shares"},{"title":"Data Preparation for Code Smell Detection: A Literature Review","url":"/papers/ssrn/4693778/","summary":"A Literature Review: The review examines data preparation techniques in deep learning-based code smell detection, suggesting ways to prepare high-quality data and emphasizing the need for data diversity, standardization, and accessibility.","featured":"2024-01-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Prototype-based Models for Real Estate Valuation: A Machine Learning Model That Explains Prices","url":"/papers/ssrn/4695079/","summary":"The article introduces a novel real estate valuation model that uses prototype-based learning, a method that compares properties to similar ones, unlike traditional machine learning methods.","featured":"2024-01-17","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"From Meteorology to Market: A Geo-Hierarchical Deep Learning Approach for Flood Risk Pricing","url":"/papers/ssrn/4692475/","summary":"Geo-Hierarchical Deep Learning: A new deep learning framework enhances flood risk modeling, providing more accurate pricing and reducing capital requirements, as shown in a Mississippi River case study.","featured":"2024-01-17","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Boosting Fund Performance","url":"/papers/ssrn/4697785/","summary":"Mutual funds that match their investments with similar benchmark peers like the S&P 500 index yield higher returns and experience less volatility.","featured":"2024-01-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Transformers are Multi-State RNNs","url":"/papers/arxiv/2401.06104/","summary":"The research shows that decoder-only transformers can be seen as infinite multi-state RNNs and introduces a new policy, TOVA, which performs better in long-range tasks and uses less memory.","featured":"2024-01-17","label":"Machine learning","topic":"ML & AI Methods","cites":128,"score":152,"scale":"shares"},{"title":"Federated Incremental Learning Algorithm","url":"/papers/ssrn/4683585/","summary":"The paper introduces a new learning algorithm that uses Topological Data Analysis to prevent local models from forgetting previous knowledge and improve server feature capture.","featured":"2024-01-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Network of Economic Sectors and Return Prediction -> Economic Sector Network and Return Prediction","url":"/papers/ssrn/4683418/","summary":"The study employs a hybrid machine learning method, CNN-LSTM, to predict the interconnectedness of economic sectors in emerging markets, showing its effectiveness in improving prediction accuracy.","featured":"2024-01-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for Firm Quality Measure.","url":"/papers/ssrn/4682724/","summary":"Machine learning outperforms human experts in evaluating firm quality, with the residual income valuation method proving superior to the DuPont method and extensive data mining.","featured":"2024-01-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Artificial Intelligence & Private Equity Fund Perf.","url":"/papers/ssrn/4684754/","summary":"Private equity fund performance doesn't correlate with quantitative data like past performance, but machine learning can predict future performance using qualitative data.","featured":"2024-01-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Reinforcement Learning and Rational Expectations Equilibrium in Limit Order Markets","url":"/papers/ssrn/4682574/","summary":"A paper suggests that simple payoff-based reinforcement learning can help achieve rational expectations equilibrium in limit order markets, with speculators mainly providing liquidity.","featured":"2024-01-09","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Generating synthetic data for neural operators","url":"/papers/arxiv/2401.02398/","summary":"A novel method for creating synthetic functional training data for deep learning solutions to partial differential equations (PDEs) is proposed, eliminating the need for a numerical PDE solver and potentially broadening the scope for developing neural PDE solvers.","featured":"2024-01-09","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":17,"scale":"shares"},{"title":"Machine Learning for Synthetic Data Generation: a Review","url":"/papers/arxiv/2302.04062/","summary":"A Review: The article reviews machine learning models for creating synthetic data, discussing their uses, methods, privacy issues, fairness, and future research opportunities in fields like computer vision, speech, natural language processing, healthcare, and business.","featured":"2024-01-09","label":"Machine learning","topic":"ML & AI Methods","cites":309,"score":38,"scale":"shares"},{"title":"On the hardness of learning under symmetries","url":"/papers/arxiv/2401.01869/","summary":"The process of learning neural networks through gradient descent is complex, despite the advantages of integrating known symmetries, as indicated by lower bounds for various network types.","featured":"2024-01-09","label":"Machine learning","topic":"ML & AI Methods","cites":18,"score":18,"scale":"shares"},{"title":"Machine Learning for Political Firm Identification","url":"/papers/repec/bla-obuest-v-86-y-2024-i-1-p-137-155/","summary":"Machine learning is used to identify politically connected firms in Czechia with 85% accuracy, suggesting its use in detecting conflicts of interest.","featured":"2024-01-09","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"A Brief History of General-to-Specific Modelling","url":"/papers/repec/bla-obuest-v-86-y-2024-i-1-p-1-20/","summary":"The article discusses the evolution of general-to-specific modelling from manual to automated machine learning, addressing criticisms and its ability to handle non-stationary data.","featured":"2024-01-09","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Profitability Prediction in Europe Using Machine Learning","url":"/papers/repec/gam-jjrfmx-v-16-y-2023-i-12-p-520-d-1302258/","summary":"The study uses machine learning algorithms to predict profitability direction in Europe, finding that simpler algorithms can outperform more complex ones with the right data preprocessing.","featured":"2024-01-09","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions","url":"/papers/arxiv/2312.16223/","summary":"The article introduces a SHAP-based explainability technique for financial forecasting, increasing transparency and confidence in the stock exchange market.","featured":"2024-01-03","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":3,"scale":"shares"},{"title":"A graph-based multimodal framework to predict gentrification","url":"/papers/arxiv/2312.15646/","summary":"A new machine learning model can predict gentrification using socioeconomic data and images, highlighting a significant connection between gentrification and schools.","featured":"2024-01-03","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":5,"scale":"shares"},{"title":"Changepoint Detection Approach Using Deep Learning","url":"/papers/ssrn/4675568/","summary":"The study presents a method for identifying change points in time series data, including financial data, using a trained neural network, offering new tools for financial market analysis.","featured":"2024-01-03","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":6,"scale":"shares"},{"title":"Economic Sector Network and Return Prediction -> Sector Network and Return Prediction","url":"/papers/ssrn/4682415/","summary":"The CNNLSTM hybrid machine learning approach enhances prediction accuracy in emerging markets' connected economic sectors.","featured":"2024-01-03","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Compact Neural Graphics Primitives with Learned Hash Probing","url":"/papers/arxiv/2312.17241/","summary":"The study introduces a hash table with learned probes for neural graphics primitives, providing a balance of size and speed, and outperforming previous index learning methods.","featured":"2024-01-03","label":"Machine learning","topic":"ML & AI Methods","cites":35,"score":8,"scale":"shares"},{"title":"SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks","url":"/papers/arxiv/2312.17216/","summary":"SparseProp, an event-based algorithm for simulating and training large-scale spiking neural networks, is introduced, offering reduced computational cost and efficient training.","featured":"2024-01-03","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":8,"scale":"shares"},{"title":"PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis","url":"/papers/arxiv/2310.00426/","summary":"PIXART-$\\alpha$, a Transformer-based text-to-image model, generates high-quality images at a low cost, reducing CO2 emissions and offering a cost-effective solution for the AIGC community.","featured":"2024-01-03","label":"Machine learning","topic":"ML & AI Methods","cites":967,"score":166,"scale":"shares"},{"title":"Revisiting inference after prediction","url":"/papers/arxiv/2306.13746/","summary":"Angelopoulos et al.'s method provides valid inference on the association between unobserved response and covariates, regardless of the quality of the pre-trained machine learning model, unlike Wang et al.'s method.","featured":"2024-01-03","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":49,"scale":"shares"},{"title":"When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions","url":"/papers/arxiv/2306.15546/","summary":"Motivations, Challenges, Future Directions: The combination of Foundation Model (FM) and Federated Learning (FL) enhances AI research by increasing data availability and improving performance and convergence speed.","featured":"2024-01-03","label":"Machine learning","topic":"ML & AI Methods","cites":142,"score":20,"scale":"shares"},{"title":"Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network","url":"/papers/arxiv/2303.16532/","summary":"A new model for predicting futures prices in high-frequency trading, using graph neural networks, outperforms existing models in China's futures market.","featured":"2023-12-20","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":23,"scale":"shares"},{"title":"Let's do the time-warp-attend: Learning topological invariants of dynamical systems","url":"/papers/arxiv/2312.09234/","summary":"Learning Topological Invariants in Dynamical Systems: The study proposes a deep-learning framework for classifying dynamical regimes and identifying bifurcation boundaries in different systems, offering insights into large-scale physical and biological systems.","featured":"2023-12-20","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":8,"scale":"shares"},{"title":"An Invitation to Deep Reinforcement Learning","url":"/papers/arxiv/2312.08365/","summary":"The guide introduces reinforcement learning as an extension of supervised learning, offering an easy-to-understand method for learning advanced deep reinforcement learning algorithms such as proximal policy optimization.","featured":"2023-12-20","label":"Machine learning","topic":"ML & AI Methods","cites":13,"score":29,"scale":"shares"},{"title":"Equity Return Prediction with Deep Learning & Ensemble Methods","url":"/papers/ssrn/4660984/","summary":"The article examines forecast combination methods in machine learning for predicting equity returns, suggesting a new performance measure for risk premium forecasts that provides more robust evaluations and economic interpretability.","featured":"2023-12-13","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform","url":"/papers/arxiv/2312.04180/","summary":"Evidence of Change: The paper explores the effect of artificial intelligence on jobs, offering a visual framework and an economic model, and presents evidence of AI's disruptive impact on translation and web development jobs.","featured":"2023-12-13","label":"arXiv","topic":"ML & AI Methods","cites":15,"score":2,"scale":"shares"},{"title":"Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs","url":"/papers/arxiv/2302.11835/","summary":"The study suggests a new reinforcement learning method for calibrating agent-based models in economics and finance, which performs better than other tested methods.","featured":"2023-12-13","label":"arXiv","topic":"ML & AI Methods","cites":14,"score":30,"scale":"shares"},{"title":"Adversarial Learning for Feature Shift Detection and Correction","url":"/papers/arxiv/2312.04546/","summary":"The study investigates the use of adversarial learning to identify and correct feature shifts in datasets, demonstrating that it can outperform current statistical and neural network-based methods when combined with mainstream supervised classifiers.","featured":"2023-12-13","label":"Machine learning","topic":"ML & AI Methods","cites":5,"score":18,"scale":"shares"},{"title":"Gated Linear Attention Transformers with Hardware-Efficient Training","url":"/papers/arxiv/2312.06635/","summary":"Efficient Training of Gated Linear Attention Transformers: The research introduces a more hardware-efficient version of gated linear attention Transformers that performs well against other models, especially in training on longer sequences.","featured":"2023-12-13","label":"Machine learning","topic":"ML & AI Methods","cites":539,"score":150,"scale":"shares"},{"title":"Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit","url":"/papers/arxiv/2309.16620/","summary":"A new method for hyperparameter tuning in deep learning has been proposed, using residual networks and a specific parameterization for optimal hyperparameter transfer across network width and depth.","featured":"2023-12-13","label":"Machine learning","topic":"ML & AI Methods","cites":69,"score":106,"scale":"shares"},{"title":"Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia","url":"/papers/arxiv/2312.03664/","summary":"Concordia is a library designed to help build and operate Generative Agent-Based Models (GABMs), using Large Language Models (LLMs) to simulate physical or digital environments.","featured":"2023-12-13","label":"Machine learning","topic":"ML & AI Methods","cites":164,"score":83,"scale":"shares"},{"title":"Sports Performance Analysis with ML and Statistical Models","url":"/papers/repec/blg-reveco-v-75-y-2023-i-2-p-34-39/","summary":"Machine learning can be used in sports prediction, especially in football, to develop strategies for maximizing revenue, with a paper outlining the required steps in data processing and analysis.","featured":"2023-12-13","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":15,"scale":"shares"},{"title":"Political Clustering in Vietnam Stock Exchange","url":"/papers/repec/wsi-jicepx-v-14-y-2023-i-03-n-s1793993323500242/","summary":"Machine learning is used in a study to group politically affiliated businesses, suggesting it can replace traditional methods and moderately political businesses perform better.","featured":"2023-12-13","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":22,"scale":"shares"},{"title":"Historical calibration of SVJD models with deep learning","url":"/papers/ssrn/4650097/","summary":"The paper suggests using deep neural networks to calibrate parameters of Stochastic Volatility Jump Diffusion models, proving to be more accurate, robust, and faster than other methods.","featured":"2023-12-06","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"Sharpening Sharpe Analysis with Machine Learning","url":"/papers/ssrn/4650859/","summary":"The research shows that over 95% of mutual funds have multidimensional investment styles, and those that change their styles often outperform their new style benchmarks.","featured":"2023-12-06","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Optimal Stopping via Randomized Neural Networks","url":"/papers/arxiv/2104.13669/","summary":"The article highlights the benefits of using randomized neural networks to approximate solutions for optimal stopping problems, proving they are more efficient and faster than other machine learning methods.","featured":"2023-12-06","label":"arXiv","topic":"ML & AI Methods","cites":49,"score":38,"scale":"shares"},{"title":"Survival Models for Startup Failures","url":"/papers/repec/bba-j00005-v-1-y-2023-i-3-p-1-15-d-264/","summary":"The research finds that advanced machine learning models like MTLR and Random Forest are more accurate in predicting startup failures than standard models.","featured":"2023-12-06","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":16,"scale":"shares"},{"title":"Enhanced Sample Quality with Self-Attention Guidance","url":"/papers/web/8529a2e645/","summary":"Denoising diffusion models are becoming increasingly popular due to their high-quality and diverse generation capabilities.","featured":"2023-12-06","label":"Machine learning","topic":"ML & AI Methods","cites":null,"score":16676,"scale":"shares"},{"title":"Universalizing Weak Supervision","url":"/papers/arxiv/2112.03865/","summary":"The article introduces a universal technique for weak supervision frameworks that can be applied to any label type, demonstrating improvements in various settings including learning-to-rank and regression problems.","featured":"2023-12-06","label":"Machine learning","topic":"ML & AI Methods","cites":36,"score":71,"scale":"shares"},{"title":"Edge Directionality Improves Learning on Heterophilic Graphs","url":"/papers/arxiv/2305.10498/","summary":"The study presents Directed Graph Neural Network (Dir-GNN), a new deep learning framework for directed graphs that surpasses traditional models in heterophilic benchmarks.","featured":"2023-12-06","label":"Machine learning","topic":"ML & AI Methods","cites":148,"score":186,"scale":"shares"},{"title":"Machine Learning for Path-Dependent Contracts","url":"/papers/ssrn/4646847/","summary":"The study introduces machine learning algorithms for pricing certain financial products and a new method for calculating sensitivities using Chebyshev interpolation techniques.","featured":"2023-11-29","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":5,"scale":"shares"},{"title":"Data-Driven Investment Strategies for Large-Cap Universe","url":"/papers/ssrn/4641315/","summary":"A deep learning architecture has been used to create advanced investment strategies for US stocks, outperforming the S&P 500 index.","featured":"2023-11-29","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Expected Mispricing","url":"/papers/ssrn/4638234/","summary":"The paper introduces a new measure of expected mispricing at the firm level using machine learning, which outperforms existing methods in predicting future mispricing.","featured":"2023-11-29","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":9,"scale":"shares"},{"title":"Revamping Firm Fixed Effects Models with Machine Learning - New Evidence from Recovering the Missing R&D-Patent Relation","url":"/papers/ssrn/4636846/","summary":"Using firm fixed effects in corporate finance research may negate the impact of persistent economic factors, but advanced machine learning can provide alternative insights.","featured":"2023-11-29","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Generative Machine Learning for Multivariate Equity Returns","url":"/papers/doi/10-1145-3604237-3626884/","summary":"The study uses machine learning techniques to model the returns of S&P 500 equities.","featured":"2023-11-29","label":"arXiv","topic":"ML & AI Methods","cites":8,"score":9,"scale":"shares"},{"title":"Machine-learning regression methods for American-style path-dependent contracts","url":"/papers/arxiv/2311.16762/","summary":"The paper presents a comparison of machine learning algorithms for pricing financial products with early-termination features and introduces a new method for calculating sensitivities.","featured":"2023-11-29","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Quantum-inspired nonlinear Galerkin ansatz for high-dimensional HJB equations","url":"/papers/arxiv/2311.12239/","summary":"Quantum-inspired Nonlinear Galerkin Ansatz for High-dimensional PDEs: The research investigates the use of Neural Galerkin methods in solving Hamilton-Jacobi-Bellman partial differential equations, offering trial functions with solvable evolution equations.","featured":"2023-11-29","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":4,"scale":"shares"},{"title":"Real Estate Appraisals: ML vs Traditional Methods","url":"/papers/repec/spr-gjorer-v-9-y-2023-i-2-d-10-1365-s41056-022-00063-1/","summary":"ML vs Traditional Methods: Research indicates that XGBoost, a machine learning method, offers the most precise estimates in automated property valuations, suggesting a need for regulators to use multiple methods.","featured":"2023-11-29","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":31,"scale":"shares"},{"title":"More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory","url":"/papers/arxiv/2311.14646/","summary":"Optimal Overparameterization: The paper offers theoretical support for the idea that larger models, more data, and increased computation enhance performance in random feature regression, a type of model similar to shallow networks.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":23,"score":66,"scale":"shares"},{"title":"$\\sigma$-PCA: a building block for neural learning of identifiable linear transformations","url":"/papers/arxiv/2311.13580/","summary":"Unified Neural Model for PCA: The research proposes a unified neural model for PCA as single-layer autoencoders, capable of learning a semi-orthogonal transformation that reduces dimensionality and orders by variances, without rotational indeterminacy.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":16,"scale":"shares"},{"title":"Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks","url":"/papers/arxiv/2311.13180/","summary":"The study presents a sample-efficient algorithm for high-dimensional multi-armed contextual bandits with batched feedback, achieving regret bounds similar to those in fully sequential settings with fewer batches.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":3,"score":17,"scale":"shares"},{"title":"In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks","url":"/papers/arxiv/2309.17290/","summary":"Memory Mechanisms: Research indicates that generative diffusion models, a machine learning method, can be seen as energy-based models and can help understand how long-term memory is formed, connecting creativity and memory recall.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":66,"score":703,"scale":"shares"},{"title":"Banach-Tarski Embeddings and Transformers","url":"/papers/arxiv/2311.09387/","summary":"Interpretable Transformers: A novel method for embedding recursive data structures into high-dimensional vectors has been developed, offering an interpretable model for transformer's latent state vectors and enabling computations without decoding.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":79,"scale":"shares"},{"title":"VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch","url":"/papers/arxiv/2211.09945/","summary":"Streamlining Verified Robust Compressed Neural Networks: VeriCompress, a new tool that automates the search and training of compressed models with robustness guarantees, has been launched, providing faster training, improved accuracy, and reduced memory and inference time for deployment on resource-limited platforms.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":22,"scale":"shares"},{"title":"Topological properties of basins of attraction and expressiveness of width bounded neural networks","url":"/papers/arxiv/2011.04923/","summary":"A study has shown that autoencoders trained with standard SGD methods create bounded basins of attraction around their training data, answering a previous question and providing insight into why certain neural network functions are not dense in continuous function spaces.","featured":"2023-11-29","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":22,"scale":"shares"},{"title":"Predicting Stock Market Trends with Machine Learning: A Comprehensive Study","url":"/papers/ssrn/4629045/","summary":"The article presents a study on the use of LSTM neural networks and linear regression for stock market prediction, showing superior performance over traditional models.","featured":"2023-11-15","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":4,"scale":"shares"},{"title":"Editing Prioritization in Survey Data with Machine Learning","url":"/papers/ssrn/4632471/","summary":"Machine learning is used to identify and correct errors in household finance survey data, with Gradient Boosting Trees being the most effective method.","featured":"2023-11-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine learning for IBNR frequencies in non-life reserving","url":"/papers/ssrn/4633059/","summary":"The research introduces a machine learning model for predicting the number of Incurred But Not Reported (IBNR) claims, proving its effectiveness through a study using both simulated and real data.","featured":"2023-11-15","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment","url":"/papers/arxiv/2311.07071/","summary":"Boosting Market Prosperity and Dismissing Depression Concerns: A study finds that generative AI can lower average prices in product markets, increase order volume and revenue, and potentially benefit artists rather than causing unemployment.","featured":"2023-11-15","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":4,"scale":"shares"},{"title":"Enhancing actuarial non-life pricing models via transformers","url":"/papers/arxiv/2311.07597/","summary":"The paper presents new methods to improve non-life actuarial models with transformer models for tabular data, showing better results than benchmark models.","featured":"2023-11-15","label":"arXiv","topic":"ML & AI Methods","cites":13,"score":5,"scale":"shares"},{"title":"A Coefficient Makes SVRG Effective","url":"/papers/arxiv/2311.05589/","summary":"The article introduces α-SVRG, a new method for optimizing neural networks that improves training loss reduction across various architectures and datasets.","featured":"2023-11-15","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":16,"scale":"shares"},{"title":"Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection","url":"/papers/doi/10-2760-46796/","summary":"Cloud Removal and Urban Change Detection: Diffusion models in AI can improve Earth observation data, aiding in tasks like cloud removal, change-detection dataset creation, and urban replanning.","featured":"2023-11-15","label":"Machine learning","topic":"ML & AI Methods","cites":7,"score":69,"scale":"shares"},{"title":"Greedy PIG: Adaptive Integrated Gradients","url":"/papers/arxiv/2311.06192/","summary":"Feature Attribution: The authors suggest a unified discrete optimization framework for feature attribution and selection in deep learning models, introducing an adaptive method called Greedy PIG that performs well in various tasks.","featured":"2023-11-15","label":"Machine learning","topic":"ML & AI Methods","cites":0,"score":11,"scale":"shares"},{"title":"Survival Instinct in Offline Reinforcement Learning","url":"/papers/arxiv/2306.03286/","summary":"Survival: Offline reinforcement learning algorithms can still create effective policies even with incorrect reward labels due to their inherent pessimism and biases in data collection.","featured":"2023-11-15","label":"Machine learning","topic":"ML & AI Methods","cites":26,"score":110,"scale":"shares"},{"title":"Comparative Study of Methods to Identify Sensitive Parameters","url":"/papers/repec/ids-ijbsre-v-17-y-2023-i-6-p-636-658/","summary":"The article discusses how supervised machine learning models assign weights to input parameters to achieve the desired outcome, stressing the importance of reliable weights early in the model development process.","featured":"2023-11-15","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Identifying Dominance Regimes in the Euro Area with Machine Learning","url":"/papers/ssrn/4622203/","summary":"Machine learning has identified periods of fiscal dominance in the euro area from 2000 to 2019, including during the financial and sovereign debt crises.","featured":"2023-11-08","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Corporate Culture and Takeover Vulnerability: Evidence from Machine Learning and Earnings Conference Calls","url":"/papers/ssrn/4626185/","summary":"Research using machine learning indicates that the threat of hostile takeovers can significantly weaken a company's culture, supporting the managerial myopia hypothesis.","featured":"2023-11-08","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":3,"scale":"shares"},{"title":"Risk of Transfer Learning and its Applications in Finance","url":"/papers/arxiv/2311.03283/","summary":"The paper discusses the concept of transfer risk in transfer learning, showing its significant relation with performance and its effectiveness in selecting suitable source tasks in stock return prediction and portfolio optimization.","featured":"2023-11-08","label":"arXiv","topic":"ML & AI Methods","cites":10,"score":2,"scale":"shares"},{"title":"Idempotent Generative Network","url":"/papers/arxiv/2311.01462/","summary":"A new generative modeling method is suggested, using an idempotent neural network to project any input into a target data distribution.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":28,"score":157,"scale":"shares"},{"title":"Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time","url":"/papers/arxiv/2311.01435/","summary":"UHL: A new algorithm is introduced that can learn high-dimensional halfspaces in d-dimensional space in polynomial time, without needing labels.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":1,"score":11,"scale":"shares"},{"title":"Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization","url":"/papers/arxiv/2311.03351/","summary":"Unifying RL: Uni-o4 is a novel method that merges offline and online reinforcement learning, enhancing the adaptability of the learning process.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":38,"score":5,"scale":"shares"},{"title":"PPI++: Efficient Prediction-Powered Inference","url":"/papers/arxiv/2311.01453/","summary":"Efficient Inference: PPI++ is a new approach that utilizes a small labeled dataset and a larger machine-learning predictions dataset to boost computational and statistical efficiency.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":135,"score":5,"scale":"shares"},{"title":"CodeFusion: A Pre-trained Diffusion Model for Code Generation","url":"/papers/arxiv/2310.17680/","summary":"Model for Code Generation: CodeFusion is a new model for generating code that improves on previous models by iteratively cleaning up a complete program, matching top systems in initial accuracy and surpassing them in subsequent accuracy checks.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":61,"score":3953,"scale":"shares"},{"title":"Transport meets Variational Inference: Controlled Monte Carlo Diffusions","url":"/papers/arxiv/2307.01050/","summary":"The paper introduces a new framework for sampling and generative modelling based on divergences in path space, leading to the creation of a new score-based annealed flow technique and a regularised iterative proportional fitting objective.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":22,"score":81,"scale":"shares"},{"title":"Explainable representation learning of small quantum states","url":"/papers/arxiv/2306.05694/","summary":"The research examines the learned representation of a generative model trained on two-qubit density matrices, showing a direct correlation with the entanglement measure concurrence, providing insights into machine learning of quantum states.","featured":"2023-11-08","label":"Machine learning","topic":"ML & AI Methods","cites":15,"score":22,"scale":"shares"},{"title":"Newsvendor Problem: High-Dimensional Data and Mixed-Frequency Method","url":"/papers/repec/eee-proeco-v-266-y-2023-i-c-s0925527323002748/","summary":"High-Dimensional Data and Mixed-Frequency Method: The first article explores the application of machine learning to improve demand prediction and restocking decisions in newsvendor problems, utilizing complex and varied historical data.","featured":"2023-11-08","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":27,"scale":"shares"},{"title":"Risk-Based Certification for Machine Learning with Continuous Auditing","url":"/papers/repec/spr-trosos-v-17-y-2023-i-2-d-10-1007-s12626-023-00148-w/","summary":"The second article introduces a Continuous Audit-Based Certification for MLOps systems, which uses automated audits to manage certificates, enhancing efficiency and reliability in the machine learning system.","featured":"2023-11-08","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Projected Fuzzy C-Means Algorithm","url":"/papers/ssrn/4619454/","summary":"The article proposes a new algorithm for high-dimensional data clustering in machine learning, aiming to improve performance and manage anomalous instances.","featured":"2023-11-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"KMeans Initialization","url":"/papers/ssrn/4616032/","summary":"The article highlights the role of clustering in data mining and machine learning, focusing on the Kmeans algorithm and the challenge of selecting optimal cluster centroids.","featured":"2023-11-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Batch-Stochastic Sub-Gradient Method for Non-Smooth Loss Functions","url":"/papers/ssrn/4614051/","summary":"The new machine learning method, Batchstochastic Subgradient, offers stable loss value estimates and is more memory efficient, as demonstrated using SQL.","featured":"2023-11-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"An Empirical Analysis of Optimal Nonlinear Pricing","url":"/papers/arxiv/2302.11643/","summary":"A paper proposes a method to calculate the best price schedule considering consumer diversity in continuous-choice situations, demonstrating that optimal price discrimination can boost a firm's profit by at least 5.5% compared to linear pricing.","featured":"2023-11-02","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":43,"scale":"shares"},{"title":"TabR: Tabular Deep Learning Meets Nearest Neighbors","url":"/papers/arxiv/2307.14338/","summary":"Tabular DL Meets Nearest Neighbors: TabR, a new deep learning model for tabular data, outperforms existing models by using a k-Nearest-Neighbors-like component for better predictions.","featured":"2023-11-02","label":"arXiv","topic":"ML & AI Methods","cites":117,"score":68,"scale":"shares"},{"title":"Optimizing Levenberg-Marquardt Hyperparameters","url":"/papers/ssrn/4610057/","summary":"The effectiveness of the Levenberg-Marquardt algorithm in solving Complex Nonlinear Least Squares problems can be enhanced by using machine learning tools and adjusting hyperparameter values.","featured":"2023-10-25","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Enhanced Local Explainability and Trust Scores with Random Forest Proximities","url":"/papers/arxiv/2310.12428/","summary":"The study introduces a new method to explain the performance of random forest models by viewing them as adaptive weighted K nearest-neighbors models, offering a localized understanding of model predictions.","featured":"2023-10-25","label":"arXiv","topic":"ML & AI Methods","cites":5,"score":7,"scale":"shares"},{"title":"What Algorithms can Transformers Learn? A Study in Length Generalization","url":"/papers/arxiv/2310.16028/","summary":"The study suggests that Transformers can demonstrate strong length generalization on tasks that can be solved by a short RASP program applicable to all input lengths.","featured":"2023-10-25","label":"Machine learning","topic":"ML & AI Methods","cites":218,"score":93,"scale":"shares"},{"title":"ManifoldNeRF: View-dependent Image Feature Supervision for Few-shot Neural Radiance Fields","url":"/papers/arxiv/2310.13670/","summary":"View-dependent Feature Supervision: ManifoldNeRF is a proposed method that uses interpolated features from known viewpoints to supervise feature vectors at unknown viewpoints, enhancing novel view synthesis in Neural Radiance Fields.","featured":"2023-10-25","label":"Machine learning","topic":"ML & AI Methods","cites":6,"score":18,"scale":"shares"},{"title":"Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis","url":"/papers/arxiv/2310.13669/","summary":"A new method for automatically gathering data for reinforcement learning training of Code Synthesis models has been introduced, enhancing the performance of a pre-trained code language model.","featured":"2023-10-25","label":"Machine learning","topic":"ML & AI Methods","cites":8,"score":9,"scale":"shares"},{"title":"Size Reduction in ML Models with Pyramid Training","url":"/papers/ssrn/4604572/","summary":"A study presents a method to reduce the size of machine learning models, making research more accessible for those with limited hardware.","featured":"2023-10-18","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Big Data From the Standpoint of a Machine Learning Approach","url":"/papers/ssrn/4601119/","summary":"A survey reviews different machine learning techniques and their uses, emphasizing the need for appropriate technique selection for data analysis.","featured":"2023-10-18","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"How does artificial intelligence improve human decision‐making? Evidence from the AI‐powered Go program","url":"/papers/arxiv/2310.08704/","summary":"A study reveals that humans enhance their Go game skills after learning from an AI-powered program, with younger players and those from AI-exposed countries showing more improvement.","featured":"2023-10-18","label":"arXiv","topic":"ML & AI Methods","cites":40,"score":4,"scale":"shares"},{"title":"Optimal investment in ambiguous financial markets with learning","url":"/papers/arxiv/2303.08521/","summary":"The paper presents a solution for the multi-asset Merton investment problem under drift uncertainty, exploring the influence of drift distribution and ambiguity preferences on investment strategies.","featured":"2023-10-18","label":"arXiv","topic":"ML & AI Methods","cites":10,"score":20,"scale":"shares"},{"title":"Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining","url":"/papers/arxiv/2310.08566/","summary":"The article presents a theoretical framework for training large transformer models for in-context reinforcement learning, offering the first quantitative analysis of their capabilities.","featured":"2023-10-16","label":"Machine learning","topic":"ML & AI Methods","cites":90,"score":10,"scale":"shares"},{"title":"Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks","url":"/papers/arxiv/2310.07711/","summary":"The study uses a brain-inspired modular training method in machine learning to improve network performance and neuron clustering in compositional cognitive tasks.","featured":"2023-10-16","label":"Machine learning","topic":"ML & AI Methods","cites":12,"score":10,"scale":"shares"},{"title":"Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning","url":"/papers/arxiv/2310.06793/","summary":"The research proposes new reinforcement learning algorithms for matrix estimation problems with low-rank structure, offering improved performance guarantees.","featured":"2023-10-16","label":"Machine learning","topic":"ML & AI Methods","cites":9,"score":8,"scale":"shares"},{"title":"Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video","url":"/papers/arxiv/2310.08584/","summary":"The study presents a new self-supervised image pretraining method using continuous videos, showing that a single video can compete with ImageNet for various tasks.","featured":"2023-10-16","label":"Machine learning","topic":"ML & AI Methods","cites":49,"score":3,"scale":"shares"},{"title":"Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks","url":"/papers/arxiv/2310.02244/","summary":"Deep Residual Network Feature Learning: The research explores depthwise parametrizations in deep residual networks, pinpointing Depth-$\\mu$P as the best parametrization for maximizing feature learning and diversity, but notes its limitations in deeper networks.","featured":"2023-10-16","label":"Machine learning","topic":"ML & AI Methods","cites":103,"score":53,"scale":"shares"},{"title":"Machine Learning & the Re-Enchantment of the Admin State","url":"/papers/ssrn/4596562/","summary":"The article discusses the conflict between the use of complex machine learning algorithms in administrative decisions and the need for explanation in administrative law, hinting at possible systemic impacts.","featured":"2023-10-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Fraud Detection Literature Review","url":"/papers/ssrn/4594459/","summary":"The study introduces a framework for using machine learning in financial statement fraud literature analysis, utilizing bibliometric analysis techniques and topic modeling.","featured":"2023-10-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Privacy Preserving Machine Learning","url":"/papers/ssrn/4595287/","summary":"The tutorial explains how to use machine learning on encrypted data, which is beneficial for managing sensitive personal data under regulations like GDPR.","featured":"2023-10-12","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":5,"scale":"shares"},{"title":"Does Artificial Intelligence benefit UK businesses? An empirical study of the impact of AI on productivity","url":"/papers/arxiv/2310.05985/","summary":"No Effect on Labour Productivity: The paper finds no significant effect of AI adoption on labour productivity in UK businesses from 2015 to 2019, based on data from the UK Office for National Statistics.","featured":"2023-10-12","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":4,"scale":"shares"},{"title":"Predicting China's CPI by Scanner Big Data","url":"/papers/arxiv/2211.16641/","summary":"The study uses supermarket sales data to create a Food Consumer Price Index in China and uses machine learning to predict CPI growth rate, performing better than traditional models.","featured":"2023-10-12","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":11,"scale":"shares"},{"title":"Gender Diversity Prediction in Chinese Boardrooms with Machine Learning","url":"/papers/repec/eee-riibaf-v-66-y-2023-i-c-s0275531923001794/","summary":"A study successfully used machine learning, specifically the XGBoost model, to predict gender diversity on the boards of Chinese publicly-traded companies.","featured":"2023-10-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":22,"scale":"shares"},{"title":"XGBoost Framework for Fraud Detection in Mobile Payments","url":"/papers/repec/spr-infosf-v-25-y-2023-i-5-d-10-1007-s10796-022-10346-6/","summary":"A proposed XGBoost-based framework for detecting fraud in mobile transactions was found to be most effective when combined with multiple unsupervised outlier detection algorithms.","featured":"2023-10-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Comparing ML Algorithms for Item Difficulty Prediction","url":"/papers/repec/gam-jmathe-v-11-y-2023-i-19-p-4104-d-1249993/","summary":"A comparison of machine learning methods for predicting English reading comprehension test difficulty found that elastic net was best for continuous prediction, while random forests excelled in classification tasks.","featured":"2023-10-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Machine learning in algorithmic investment strategies on global stock markets","url":"/papers/repec/eee-riibaf-v-66-y-2023-i-c-s0275531923001782/","summary":"Algorithmic investment strategies using machine learning models perform better than passive strategies, with Linear Support Vector Machine and Bayesian Generalized Linear Model being the most effective, research shows.","featured":"2023-10-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":31,"scale":"shares"},{"title":"ML and Trade Agreements","url":"/papers/repec/kap-openec-v-34-y-2023-i-4-d-10-1007-s11079-022-09685-3/","summary":"The article uses machine learning to study the effect of free trade agreement policies on trade flows, concluding that more detailed agreements have a greater impact.","featured":"2023-10-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":22,"scale":"shares"},{"title":"Predicting Stock Returns with ML","url":"/papers/repec/eee-dyncon-v-155-y-2023-i-c-s0165188923001318/","summary":"The study applies machine learning to predict stock market returns based on company traits, with results varying depending on company size, recent data availability, and market-specific elements.","featured":"2023-10-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":19,"scale":"shares"},{"title":"Revisiting the CEO Effect Through a Machine Learning Lens","url":"/papers/ssrn/4591114/","summary":"The study suggests that the influence of CEOs on their companies' performance, or the CEO effect, is not significant, based on machine learning and predictive analytics.","featured":"2023-10-04","label":"SSRN","topic":"ML & AI Methods","cites":3,"score":3,"scale":"shares"},{"title":"Elasticity of Machine Learning and Investment Strategies","url":"/papers/ssrn/4586223/","summary":"Modern asset pricing models suggest that statistical arbitrageurs create inelastic market demand for assets, a contrast to classical models where they create elastic demand.","featured":"2023-10-04","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":233,"scale":"shares"},{"title":"The Future of Tax Law: Legal Singularity Ahead","url":"/papers/ssrn/4582653/","summary":"Legal Singularity Ahead: The idea of legal singularity, where law becomes entirely comprehensive and predictable, could be achieved through AI and new technologies in tax law.","featured":"2023-09-28","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points","url":"/papers/arxiv/2309.14334/","summary":"A new machine learning framework can identify critical changes in complex systems and calculate the likelihood of rare events, using detailed spatiotemporal data.","featured":"2023-09-28","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":6,"scale":"shares"},{"title":"Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models","url":"/papers/arxiv/2309.13246/","summary":"An Investigation: The study investigates the reliability of scientific explanations when using explainable machine learning methods on science-based machine learning models, showing varying results based on the type of monotonicity.","featured":"2023-09-28","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":6,"scale":"shares"},{"title":"Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems","url":"/papers/arxiv/2309.14548/","summary":"Research indicates that recommendation algorithms on e-commerce platforms can affect the dynamics of AI-based pricing algorithms, with profit-based systems promoting collusion and demand-based systems encouraging competition.","featured":"2023-09-28","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":2,"scale":"shares"},{"title":"Stock Price Crash Prediction Based on Multimodal Data Machine Learning Models","url":"/papers/ssrn/4575784/","summary":"The paper suggests a machine learning framework that predicts stock market crashes by combining market data, graph data, and sentiment analysis, with LightGBM showing superior accuracy.","featured":"2023-09-21","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"Explainable ML Models for Cost of Capital","url":"/papers/ssrn/4575014/","summary":"The article introduces a machine learning model to evaluate the influence of financial and nonfinancial factors on a company's cost of capital, emphasizing the role of environmental performance and governance practices.","featured":"2023-09-21","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Enhancing Reliability Estimation for Numeric Predictions: ML Approach","url":"/papers/repec/inm-orijoc-v-34-y-2022-i-1-p-503-521/","summary":"ML Approach: The article presents a machine learning method to enhance the accuracy of individual predictions in numerical predictive modeling, showing notable improvements in complex predictive scenarios.","featured":"2023-09-21","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Stochastic Control with Exit Time: Policy Gradient Learning","url":"/papers/repec/taf-apmtfi-v-29-y-2022-i-6-p-439-456/","summary":"Policy Gradient Learning: The research shows that policy gradient methods for stochastic control with exit time outperform other techniques in share repurchase pricing and can adapt to realistic market conditions.","featured":"2023-09-21","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Detecting Accounting Frauds with Machine Learning","url":"/papers/ssrn/4568957/","summary":"The article introduces Logit-Boost, a new machine learning model for detecting fraud in accounting, which performs better and uses fewer predictors than other methods.","featured":"2023-09-14","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":281,"scale":"shares"},{"title":"Generating drawdown-realistic financial price paths using path signatures","url":"/papers/arxiv/2309.04507/","summary":"A novel machine learning method for simulating financial price data sequences with drawdowns is discussed, using a non-parametric Monte Carlo approach.","featured":"2023-09-14","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":6,"scale":"shares"},{"title":"A compendium of data sources for data science, machine learning, and artificial intelligence","url":"/papers/arxiv/2309.05682/","summary":"Data Sources for ML: The article provides a detailed list of data sources for multiple sectors like finance and life sciences, catering to the growing need for data in data science, machine learning, and AI.","featured":"2023-09-14","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":8,"scale":"shares"},{"title":"Football Prediction with Machine Learning","url":"/papers/repec/ids-ijbsre-v-17-y-2023-i-5-p-565-586/","summary":"The project uses machine learning models to predict English Premier League football matches outcomes with a 52.3% accuracy for the 2020-2021 season, using expected goals metric instead of traditional goals scored.","featured":"2023-09-14","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Improved Dam Break Outflow Prediction with Ensemble ML","url":"/papers/repec/spr-nathaz-v-118-y-2023-i-3-d-10-1007-s11069-023-06060-4/","summary":"The DA-IBK machine learning model excels at predicting dam break peak outflow, significantly outperforming empirical equations, particularly at high outflows.","featured":"2023-09-14","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":21,"scale":"shares"},{"title":"Determinant Analysis for Housing Price Prediction","url":"/papers/repec/eme-ijhmap-ijhma-02-2022-0025/","summary":"A regression-based machine learning model can accurately predict housing prices and identify key influencing factors.","featured":"2023-09-14","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":8,"scale":"shares"},{"title":"Disruptive Innovation and IPO Outcomes: Evidence from Machine Learning","url":"/papers/ssrn/4556095/","summary":"The paper presents a new text-based measure of disruptive innovation, developed through machine learning and textual analysis of IPO prospectuses, which accurately predicts IPO results and challenges the hype hypothesis about tech stocks.","featured":"2023-08-30","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Deep multi-step mixed algorithm for high dimensional non-linear PDEs and associated BSDEs","url":"/papers/arxiv/2308.14487/","summary":"A new deep learning algorithm has been developed to solve complex mathematical equations, offering improved accuracy and less complexity than similar models.","featured":"2023-08-30","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":5,"scale":"shares"},{"title":"Scenario generation for market risk models using generative neural networks","url":"/papers/arxiv/2109.10072/","summary":"The study extends the use of generative adversarial networks (GANs) to a full internal market risk model, suggesting that GAN-based models can be a data-driven alternative for market risk modeling.","featured":"2023-08-30","label":"arXiv","topic":"ML & AI Methods","cites":22,"score":31,"scale":"shares"},{"title":"Designing an Attack-Defense Game: How to Increase the Robustness of Financial Transaction Models Via a Competition","url":"/papers/arxiv/2308.11406/","summary":"An Attack-Defense Game: The research explores the strengths and weaknesses of neural network models in finance by conducting a competition, offering insights on model security and suggesting new attack or defense strategies.","featured":"2023-08-24","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Bridging the Gap in Legal Document Analysis","url":"/papers/ssrn/4545060/","summary":"The article promotes the application of legal theory in machine learning to extract information from legal texts, with a focus on the interest theory of rights and the Hohfeldian taxonomy of legal relations.","featured":"2023-08-24","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"A New Approach to Overcoming Zero Trade in Gravity Models to Avoid Indefinite Values in Linear Logarithmic Equations and Parameter Verification Using Machine Learning","url":"/papers/arxiv/2308.06303/","summary":"The paper suggests a two-step method involving linear regression and machine learning to calculate gravity parameters in global trade, tackling the issue of zero flow trades.","featured":"2023-08-17","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Deep Learning for Personality Measurement","url":"/papers/repec/inm-orisre-v-34-y-2023-i-1-p-194-222/","summary":"The second article introduces DeepPerson, a tool for text-based personality detection that merges psycholinguistic theories with deep learning strategies, with the goal of enhancing the precision of personality assessments for improved predictive analytics in organizations and consumer decision making.","featured":"2023-08-17","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Machine Learning for Housing Prices","url":"/papers/repec/eme-ijhmap-ijhma-02-2022-0033/","summary":"Housing price trends can be accurately predicted by machine learning algorithms considering land use-transportation interactions, physical conditions, and socio-economic factors.","featured":"2023-08-17","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":21,"scale":"shares"},{"title":"Machine Learning for Lag Selection in Finance Research","url":"/papers/ssrn/4543446/","summary":"Random Regression Forests (RRF) are more effective than traditional methods and other machine learning techniques in choosing optimal lags for forecasting in various data series.","featured":"2023-08-17","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Investment Grade Rating with Explainable AI","url":"/papers/ssrn/4535478/","summary":"The article explores the use of explainable AI in formulating rules on financial ratios to help companies enhance their credit ratings.","featured":"2023-08-09","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Reinforcement Learning for Financial Index Tracking","url":"/papers/ssrn/4532072/","summary":"Reinforcement Learning and Deep RL Method: A new model for tracking financial indices has been proposed, which improves on existing models by including market information variables, exact transaction cost calculation, and new decision variables for cash injection or withdrawal.","featured":"2023-08-09","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":3,"scale":"shares"},{"title":"Reinforcement Learning for Financial Index Tracking","url":"/papers/arxiv/2308.02820/","summary":"The article suggests a new dynamic model for tracking financial indexes, which overcomes existing model limitations and offers better accuracy and profit potential.","featured":"2023-08-09","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy","url":"/papers/arxiv/2308.02914/","summary":"A study using Graph Neural Networks to identify anomalies in global financial markets found that the interconnected structure of highly correlated assets decreases during a crisis, with the number of anomalies varying based on the crisis stage.","featured":"2023-08-09","label":"arXiv","topic":"ML & AI Methods","cites":4,"score":2,"scale":"shares"},{"title":"AI exposure predicts unemployment risk","url":"/papers/arxiv/2308.02624/","summary":"Research indicates that individual AI exposure models don't predict unemployment or job separation rates, but a combination of these models does, highlighting the need for dynamic, context-aware AI exposure assessment methods.","featured":"2023-08-09","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":2,"scale":"shares"},{"title":"Online learning techniques for prediction of temporal tabular datasets with regime changes","url":"/papers/arxiv/2301.00790/","summary":"A machine learning pipeline is suggested for ranking predictions on temporal panel datasets, showing improved performance with Gradient Boosting Decision Trees models.","featured":"2023-08-09","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":38,"scale":"shares"},{"title":"Computing Multi-Eigenpairs with Tensor Networks","url":"/papers/ssrn/4523254/","summary":"The paper introduces a tensor-neural-network-based machine learning method to accurately compute multi-eigenpairs of high dimensional eigenvalue problems.","featured":"2023-08-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for Exam Fraud Detection","url":"/papers/ssrn/4523598/","summary":"A study successfully uses machine learning to detect exam cheating, enhancing the credibility of the examination system.","featured":"2023-08-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Jump-Diffusion Model for Climate Risk Assessment","url":"/papers/ssrn/4523784/","summary":"A stochastic asset pricing model assesses climate risk at the firm level, examining the impact of climate-related risk factors on stock return volatility and market return correlations.","featured":"2023-08-02","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":78,"scale":"shares"},{"title":"Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations","url":"/papers/arxiv/2210.07184/","summary":"The study uses deep reinforcement learning to balance hedging and skewing in a game between liquidity providers and takers in an over-the-counter market, introducing a new algorithm to impose constraints on the game's equilibrium.","featured":"2023-08-02","label":"arXiv","topic":"ML & AI Methods","cites":23,"score":139,"scale":"shares"},{"title":"Predicting Corporate Fraud with Machine Learning","url":"/papers/repec/kap-jbuset-v-186-y-2023-i-1-d-10-1007-s10551-022-05120-2/","summary":"The study applies a machine learning model using the GONE framework to predict corporate fraud in China, revealing that the Random Forest model is superior and that exposure variables are vital for accurate prediction.","featured":"2023-08-02","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Auto ML Technique Analysis","url":"/papers/ssrn/4516309/","summary":"The paper reviews Auto Machine Learning (AutoML), its pros and cons, and potential future research, highlighting its role in enhancing model accuracy and minimizing human intervention.","featured":"2023-07-26","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for Automating Monitoring, Review and Testing at Financial Institutions","url":"/papers/ssrn/4519923/","summary":"The article investigates the use of machine learning for automating Volcker Rule compliance testing, a neglected area in risk management.","featured":"2023-07-26","label":"SSRN","topic":"ML & AI Methods","cites":0,"score":3,"scale":"shares"},{"title":"Deep Reinforcement Learning for Robust Goal-Based Wealth Management","url":"/papers/doi/10-1007-978-3-031-34111-3-7/","summary":"The paper suggests a new approach for goal-based wealth management using deep reinforcement learning, proving its effectiveness over several benchmarks on both simulated and historical market data.","featured":"2023-07-26","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":3,"scale":"shares"},{"title":"Robust Monitoring Machine for R2-Hacking","url":"/papers/repec/spr-fininn-v-9-y-2023-i-1-d-10-1186-s40854-023-00497-z/","summary":"The research introduces a method using collective machine learning to prevent data manipulation in test samples, enhancing the accuracy and consistency of stock return predictions and preventing R^2-hacking issues.","featured":"2023-07-26","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":12,"scale":"shares"},{"title":"Facial Characteristics & Returns in Economics","url":"/papers/repec/sae-jospec-v-24-y-2023-i-6-p-737-758/","summary":"The paper applies computer vision and machine learning to identify facial traits of college football coaches, suggesting a salary bias against attractiveness and favoring aggressiveness.","featured":"2023-07-26","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":13,"scale":"shares"},{"title":"Risk Model of Machine Learning in Software Project Development","url":"/papers/ssrn/4511875/","summary":"The research identifies key risk factors causing failures in machine learning-based software projects, creating a risk model through literature review and expert surveys.","featured":"2023-07-19","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning for Fake Job Detection","url":"/papers/ssrn/4508792/","summary":"The research introduces a machine learning technique to detect and halt fraudulent online job advertisements, safeguarding job hunters from scams.","featured":"2023-07-19","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Machine Learning Alpha in Stock Return Predictions","url":"/papers/ssrn/4514890/","summary":"Machine learning models can predict stock returns effectively when trained on longer prediction periods and paired with efficient portfolio rules.","featured":"2023-07-19","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":2,"scale":"shares"},{"title":"Averaging plus learning models and their asymptotics","url":"/papers/arxiv/1904.08131/","summary":"The paper introduces unique models for agents interacting in financial markets and social networks, where unexpected events act as news, and agents learn from what they observe, offering fresh perspectives on social learning models.","featured":"2023-07-19","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":24,"scale":"shares"},{"title":"Choice Model vs. ML Techniques for Vehicle Ownership Decisions","url":"/papers/repec/eee-transa-v-173-y-2023-i-c-s0965856423001477/","summary":"The piece explores the use of Machine Learning as an alternative to discrete choice models in planning, indicating that their predictive performance may differ based on context and comparison metrics.","featured":"2023-07-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":17,"scale":"shares"},{"title":"Cement Tracking with Satellites and Neural Networks","url":"/papers/repec/bfr-banfra-917/","summary":"A new three-step machine learning method for predicting world trade has been proposed, which outperforms traditional linear, non-linear techniques and other benchmark models.","featured":"2023-07-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":18,"scale":"shares"},{"title":"ML Approach for Predicting Pig Iron Production","url":"/papers/repec/inm-orinte-v-51-y-2021-i-3-p-213-235/","summary":"Machine learning is used to predict production levels in pig iron plants, aiding in improving efficiency and identifying key input variables.","featured":"2023-07-19","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":19,"scale":"shares"},{"title":"SMARTboost: Efficient Tabular Learning","url":"/papers/ssrn/4501547/","summary":"Efficient Tabular Learning: SMARTboost, a new machine learning model, is designed to fit complex functions in large dimensions, adjust model complexity, manage various features, and cater to specific financial needs.","featured":"2023-07-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":398,"scale":"shares"},{"title":"Financial Machine Learning","url":"/papers/ssrn/4501707/","summary":"A Survey: A review of the emerging literature on machine learning in financial markets identifies promising research areas and provides insights for financial economists and machine learners.","featured":"2023-07-12","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":14,"scale":"shares"},{"title":"ML, AI, and PCA Insights for Profitability Prediction","url":"/papers/ssrn/4502775/","summary":"AI and machine learning models have proven effective in predicting the profitability of companies listed on the China Ashare market.","featured":"2023-07-12","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":3,"scale":"shares"},{"title":"Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer","url":"/papers/arxiv/2307.05121/","summary":"A new graph neural network, STA-GT, is proposed for transaction fraud detection, effectively learning and incorporating spatial-temporal and global information.","featured":"2023-07-12","label":"arXiv","topic":"ML & AI Methods","cites":10,"score":2,"scale":"shares"},{"title":"Hawkes Model Parameter Estimation with Recurrent Neural Networks","url":"/papers/repec/eee-finlet-v-55-y-2023-i-pa-s1544612323002945/","summary":"A recurrent neural network was used to estimate parameters of a Hawkes model using high-frequency financial data, showing faster performance and similar accuracy to traditional methods, allowing for real-time volatility measurement.","featured":"2023-07-12","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":14,"scale":"shares"},{"title":"Intraday Stock Predictability Everywhere","url":"/papers/ssrn/4496917/","summary":"Machine learning techniques show consistent predictability in intraday stock returns, with nonlinear models performing better than linear models.","featured":"2023-07-05","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":4,"scale":"shares"},{"title":"Endogenous Barriers to Learning","url":"/papers/arxiv/2306.16904/","summary":"Agents' accuracy in decision-making can cause instability in certain games, and the stability of best-response dynamics depends on the learning barrier.","featured":"2023-07-05","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle","url":"/papers/arxiv/2301.12571/","summary":"Bounded regret can be achieved in recommender systems modeled as linear contextual bandits, even without exact knowledge of the linear model, using Synthetic Control Methods.","featured":"2023-07-05","label":"arXiv","topic":"ML & AI Methods","cites":2,"score":7,"scale":"shares"},{"title":"GMDH Neural Network Predicts US REIT Market Returns","url":"/papers/repec/spr-fininn-v-9-y-2023-i-1-d-10-1186-s40854-023-00486-2/","summary":"The study compares the accuracy of GMDH neural network with traditional methods in predicting the US REIT market, finding GMDH to be highly accurate.","featured":"2023-07-05","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Limited Partners versus Unlimited Machines; Artificial Intelligence and the Performance of Private Equity Funds","url":"/papers/ssrn/4490991/","summary":"Private equity fund performance is not influenced by quantitative information, but machine learning can predict future performance using qualitative information.","featured":"2023-06-28","label":"SSRN","topic":"ML & AI Methods","cites":1,"score":452,"scale":"shares"},{"title":"Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle","url":"/papers/arxiv/2306.15033/","summary":"GitHub Copilot boosts developer productivity and generative AI tools could boost global GDP by $1.5 trillion by 2030.","featured":"2023-06-28","label":"arXiv","topic":"ML & AI Methods","cites":47,"score":2,"scale":"shares"},{"title":"Learning Not to Spoof","url":"/papers/doi/10-1145-3533271-3561767/","summary":"Reinforcement learning agents in stock trading need to follow laws and regulations, experiments show how to shape their behavior.","featured":"2023-06-14","label":"arXiv","topic":"ML & AI Methods","cites":9,"score":17,"scale":"shares"},{"title":"Multi-Source Employment Statistics with Machine Learning","url":"/papers/repec/spr-metron-v-81-y-2023-i-1-d-10-1007-s40300-023-00242-7/","summary":"Machine learning used to predict individual employment status in Italy using survey data and administrative sources.","featured":"2023-06-14","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":23,"scale":"shares"},{"title":"Quantum Continual Learning for Data","url":"/papers/repec/eee-phsmap-v-620-y-2023-i-c-s0378437123003345/","summary":"Continual learning proposed as solution to catastrophic forgetting in quantum machine learning.","featured":"2023-06-14","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Machine Learning for European Stocks","url":"/papers/repec/spr-snbeco-v-3-y-2023-i-7-d-10-1007-s43546-023-00487-4/","summary":"Machine learning used to examine predictability of equity returns in European stock market, finding linear methods perform better.","featured":"2023-06-14","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":25,"scale":"shares"},{"title":"Machine Learning for Finance","url":"/papers/ssrn/4464555/","summary":"Hybrid model combining machine learning and asset-pricing models aids capital structure decisions in finance.","featured":"2023-06-07","label":"SSRN","topic":"ML & AI Methods","cites":null,"score":33,"scale":"shares"},{"title":"Explaining AI in Finance: Past, Present, Prospects","url":"/papers/arxiv/2306.02773/","summary":"Explainable AI is important in finance and further research is needed.","featured":"2023-06-07","label":"arXiv","topic":"ML & AI Methods","cites":7,"score":3,"scale":"shares"},{"title":"Swing contract pricing: With and without neural networks","url":"/papers/arxiv/2306.03822/","summary":"Two parametric approaches to price swing contracts with firm constraints provide better prices.","featured":"2023-06-07","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":2,"scale":"shares"},{"title":"Global universal approximation of functional input maps on weighted spaces","url":"/papers/arxiv/2306.03303/","summary":"Functional input neural networks can be used for uncertainty quantification in signature kernel regression.","featured":"2023-06-07","label":"arXiv","topic":"ML & AI Methods","cites":39,"score":4,"scale":"shares"},{"title":"Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices","url":"/papers/arxiv/2004.13612/","summary":"Deep Learning for PCA: Researchers have developed Denise, a deep learning-based algorithm for robust principal component analysis of covariance matrices, which is 2000 times faster than the current state-of-the-art method.","featured":"2023-06-07","label":"arXiv","topic":"ML & AI Methods","cites":3,"score":34,"scale":"shares"},{"title":"Explainable AI","url":"/papers/repec/gam-jdataj-v-7-y-2022-i-7-p-93-d-857104/","summary":"Article explores need for explainability in machine learning models.","featured":"2023-06-07","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":23,"scale":"shares"},{"title":"Duration Dependence and Heterogeneity: Learning from Early Notice of Layoff","url":"/papers/arxiv/2305.17344/","summary":"New approach to distinguish impact of duration-dependent forces and adverse selection on unemployment exit rate using DWS data and GMM.","featured":"2023-06-01","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":5,"scale":"shares"},{"title":"Deep Into the Domain Shift: Transfer Learning Through Dependence Regularization","url":"/papers/arxiv/2305.19499/","summary":"Proposing new domain adaptation approach for learning machines.","featured":"2023-06-01","label":"arXiv","topic":"ML & AI Methods","cites":15,"score":2,"scale":"shares"},{"title":"Gated Deeper Models are Effective Factor Learners","url":"/papers/arxiv/2305.10693/","summary":"Layer deep neural network proposed for predicting excess returns with improved performance and potential for optimizing investment strategies.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":0,"score":2,"scale":"shares"},{"title":"Artificial intelligence moral agent as Adam Smith's impartial spectator","url":"/papers/arxiv/2305.11519/","summary":"External tools can help with moral assessment.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":2,"scale":"shares"},{"title":"AI Regulation in the European Union: Examining Non-State Actor Preferences","url":"/papers/arxiv/2305.11523/","summary":"Non-state actors want AI regulation.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":39,"score":2,"scale":"shares"},{"title":"The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research","url":"/papers/arxiv/2305.11528/","summary":"Research agenda for global governance of AI.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":97,"score":2,"scale":"shares"},{"title":"Trustworthy, responsible and ethical artificial intelligence in manufacturing and supply chains: synthesis and emerging research questions","url":"/papers/arxiv/2305.11581/","summary":"Risks of AI in manufacturing explored, with focus on responsible and ethical AI.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":11,"score":3,"scale":"shares"},{"title":"Reinforcement Learning Policy Recommendation for Interbank Network Stability","url":"/papers/arxiv/2204.07134/","summary":"Reinforcement learning is used to analyze the effect of a policy recommendation on an artificial interbank market.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":6,"score":95,"scale":"shares"},{"title":"Neural variance reduction for stochastic differential equations","url":"/papers/arxiv/2209.12885/","summary":"Neural SDEs with control variates are proposed to reduce variance in Monte Carlo simulations in finance.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":5,"score":18,"scale":"shares"},{"title":"Deep incremental learning models for financial temporal tabular datasets with distribution shifts","url":"/papers/arxiv/2303.07925/","summary":"A robust incremental learning model is presented for regression tasks on temporal tabular datasets.","featured":"2023-05-24","label":"arXiv","topic":"ML & AI Methods","cites":1,"score":23,"scale":"shares"},{"title":"Large Generative AI Models vs Smaller Parameter Models with More Data: A Comprehensive Literature Review","url":"/papers/ssrn/4453658/","summary":"Literature review comparing large generative AI models and smaller parameter models trained on more data, discussing advantages and limitations.","featured":"2023-05-24","label":"SSRN","topic":"ML & AI Methods","cites":2,"score":2,"scale":"shares"},{"title":"Proper Generative AI Prompting for Financial Analysis","url":"/papers/ssrn/4453664/","summary":"A guide to prompt usage in generative AI for financial analysis emphasizes the importance of prompts, provides tips for effective writing, and highlights common pitfalls to avoid.","featured":"2023-05-24","label":"SSRN","topic":"ML & AI Methods","cites":20,"score":2,"scale":"shares"},{"title":"Multi-Population Mortality Modelling with Neural Networks","url":"/papers/repec/spr-decfin-v-46-y-2023-i-1-d-10-1007-s10203-022-00382-x/","summary":"Neural network model proposed for large-scale mortality modelling and forecasting with fewer parameters and improved accuracy.","featured":"2023-05-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":7,"scale":"shares"},{"title":"Transfer Learning for Selection","url":"/papers/repec/gam-jmathe-v-10-y-2022-i-3-p-432-d-737832/","summary":"Study explores knowledge transfer problem between artificially generated and existing benchmark problems in numerical optimization.","featured":"2023-05-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"},{"title":"Predicting Fund Survival with Neural Networks","url":"/papers/repec/gam-jmathe-v-9-y-2021-i-6-p-695-d-522910/","summary":"Neural networks predict mutual fund survival with 87% accuracy.","featured":"2023-05-24","label":"RePEc","topic":"ML & AI Methods","cites":null,"score":20,"scale":"shares"}],"per_quarter":{"2023 Q2":27,"2023 Q3":55,"2023 Q4":88,"2024 Q1":79,"2024 Q2":154,"2024 Q3":202,"2024 Q4":168,"2025 Q1":119,"2025 Q2":146,"2025 Q3":28,"2025 Q4":27,"2026 Q1":2,"2026 Q2":1,"2026 Q3":11}}