---
title: Transfer Learning for Selection
url: https://www.ml-quant.com/papers/repec/gam-jmathe-v-10-y-2022-i-3-p-432-d-737832/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:gam:jmathe:v:10:y:2022:i:3:p:432-:d:737832
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F10%2F3%2F432%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A10%3Ay%3A2022%3Ai%3A3%3Ap%3A432-%3Ad%3A737832
featured: 2023-05-24
citations: unknown
topic: ML & AI Methods
---


# Transfer Learning for Selection

Study explores knowledge transfer problem between artificially generated and existing benchmark problems in numerical optimization.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F10%2F3%2F432%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A10%3Ay%3A2022%3Ai%3A3%3Ap%3A432-%3Ad%3A737832
- Identifier: RePEc:gam:jmathe:v:10:y:2022:i:3:p:432-:d:737832
- Released: 2022-02-26
- First featured: Quant Letter No. 1 (2023-05-24): https://www.ml-quant.com/issues/2023-05-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): 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.
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): 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.
- [Octo: An Open-Source Generalist Robot Policy](https://www.ml-quant.com/papers/arxiv/2405.12213/): 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.
- [Mastering Diverse Domains through World Models](https://www.ml-quant.com/papers/arxiv/2301.04104/): 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.
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://www.ml-quant.com/papers/arxiv/2404.02905/): The article discusses Visual AutoRegressive modeling (VAR), a new image learning method that outperforms diffusion transformers in terms of speed, image quality, and scalability.
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://www.ml-quant.com/papers/arxiv/2405.14734/): 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.