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Information-Theoretic Generalization Bounds for Meta-Learning and Applications
Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization ga...
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| 出版年: | Entropy (Basel) |
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| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2021
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7835863/ https://ncbi.nlm.nih.gov/pubmed/33478002 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e23010126 |
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