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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2006.02047 |
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| _version_ | 1866914047504416768 |
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| author | Cao, Haoyang Guo, Xin |
| author_facet | Cao, Haoyang Guo, Xin |
| contents | This paper analyzes the training process of GANs via stochastic differential equations (SDEs). It first establishes SDE approximations for the training of GANs under stochastic gradient algorithms, with precise error bound analysis. It then describes the long-run behavior of GANs training via the invariant measures of its SDE approximations under proper conditions. This work builds theoretical foundation for GANs training and provides analytical tools to study its evolution and stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2006_02047 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | SDE approximations of GANs training and its long-run behavior Cao, Haoyang Guo, Xin Machine Learning Probability This paper analyzes the training process of GANs via stochastic differential equations (SDEs). It first establishes SDE approximations for the training of GANs under stochastic gradient algorithms, with precise error bound analysis. It then describes the long-run behavior of GANs training via the invariant measures of its SDE approximations under proper conditions. This work builds theoretical foundation for GANs training and provides analytical tools to study its evolution and stability. |
| title | SDE approximations of GANs training and its long-run behavior |
| topic | Machine Learning Probability |
| url | https://arxiv.org/abs/2006.02047 |