A Generalization Bound for a Family of Implicit Networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908812258050048 |
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| author | Fung, Samy Wu Berkels, Benjamin |
| author_facet | Fung, Samy Wu Berkels, Benjamin |
| contents | Implicit networks are a class of neural networks whose outputs are defined by the fixed point of a parameterized operator. They have enjoyed success in many applications including natural language processing, image processing, and numerous other applications. While they have found abundant empirical success, theoretical work on its generalization is still under-explored. In this work, we consider a large family of implicit networks defined parameterized contractive fixed point operators. We show a generalization bound for this class based on a covering number argument for the Rademacher complexity of these architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07427 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Generalization Bound for a Family of Implicit Networks Fung, Samy Wu Berkels, Benjamin Machine Learning 68T07 68T07 68T07 68T07 68T07 68T07 Implicit networks are a class of neural networks whose outputs are defined by the fixed point of a parameterized operator. They have enjoyed success in many applications including natural language processing, image processing, and numerous other applications. While they have found abundant empirical success, theoretical work on its generalization is still under-explored. In this work, we consider a large family of implicit networks defined parameterized contractive fixed point operators. We show a generalization bound for this class based on a covering number argument for the Rademacher complexity of these architectures. |
| title | A Generalization Bound for a Family of Implicit Networks |
| topic | Machine Learning 68T07 68T07 68T07 68T07 68T07 68T07 |
| url | https://arxiv.org/abs/2410.07427 |