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Transfer Learning with Kernel Methods
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning method...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492830/ https://www.ncbi.nlm.nih.gov/pubmed/37689796 http://dx.doi.org/10.1038/s41467-023-41215-8 |
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author | Radhakrishnan, Adityanarayanan Ruiz Luyten, Max Prasad, Neha Uhler, Caroline |
author_facet | Radhakrishnan, Adityanarayanan Ruiz Luyten, Max Prasad, Neha Uhler, Caroline |
author_sort | Radhakrishnan, Adityanarayanan |
collection | PubMed |
description | Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws. |
format | Online Article Text |
id | pubmed-10492830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104928302023-09-11 Transfer Learning with Kernel Methods Radhakrishnan, Adityanarayanan Ruiz Luyten, Max Prasad, Neha Uhler, Caroline Nat Commun Article Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws. Nature Publishing Group UK 2023-09-09 /pmc/articles/PMC10492830/ /pubmed/37689796 http://dx.doi.org/10.1038/s41467-023-41215-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Radhakrishnan, Adityanarayanan Ruiz Luyten, Max Prasad, Neha Uhler, Caroline Transfer Learning with Kernel Methods |
title | Transfer Learning with Kernel Methods |
title_full | Transfer Learning with Kernel Methods |
title_fullStr | Transfer Learning with Kernel Methods |
title_full_unstemmed | Transfer Learning with Kernel Methods |
title_short | Transfer Learning with Kernel Methods |
title_sort | transfer learning with kernel methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492830/ https://www.ncbi.nlm.nih.gov/pubmed/37689796 http://dx.doi.org/10.1038/s41467-023-41215-8 |
work_keys_str_mv | AT radhakrishnanadityanarayanan transferlearningwithkernelmethods AT ruizluytenmax transferlearningwithkernelmethods AT prasadneha transferlearningwithkernelmethods AT uhlercaroline transferlearningwithkernelmethods |