Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909321274589184 |
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| author | Patel, Vivak Varner, Christian |
| author_facet | Patel, Vivak Varner, Christian |
| contents | The presence of non-convexity in smooth optimization problems arising from deep learning have sparked new smoothness conditions in the literature and corresponding convergence analyses. We discuss these smoothness conditions, order them, provide conditions for determining whether they hold, and evaluate their applicability to training a deep linear neural network for binary classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks Patel, Vivak Varner, Christian Machine Learning Optimization and Control 65K10, 68T07 The presence of non-convexity in smooth optimization problems arising from deep learning have sparked new smoothness conditions in the literature and corresponding convergence analyses. We discuss these smoothness conditions, order them, provide conditions for determining whether they hold, and evaluate their applicability to training a deep linear neural network for binary classification. |
| title | Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks |
| topic | Machine Learning Optimization and Control 65K10, 68T07 |
| url | https://arxiv.org/abs/2409.13672 |