Deep neural networks from the perspective of ergodic theory
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916614932267008 |
|---|---|
| author | Zhang, Fan |
| author_facet | Zhang, Fan |
| contents | The design of deep neural networks remains somewhat of an art rather than precise science. By tentatively adopting ergodic theory considerations on top of viewing the network as the time evolution of a dynamical system, with each layer corresponding to a temporal instance, we show that some rules of thumb, which might otherwise appear mysterious, can be attributed heuristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_03888 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep neural networks from the perspective of ergodic theory Zhang, Fan Machine Learning Disordered Systems and Neural Networks The design of deep neural networks remains somewhat of an art rather than precise science. By tentatively adopting ergodic theory considerations on top of viewing the network as the time evolution of a dynamical system, with each layer corresponding to a temporal instance, we show that some rules of thumb, which might otherwise appear mysterious, can be attributed heuristics. |
| title | Deep neural networks from the perspective of ergodic theory |
| topic | Machine Learning Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2308.03888 |