Journal article
Neural SDEs as infinite-dimensional GANs
- Abstract:
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Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics. However, a fundamental limitation has been that such models have typically been relatively inflexible, which recent work introducing Neural SDEs has sought to solve. Here, we show that the current classical approach to fitting SDEs may be approached as a special case of (Wasserstein) GANs, and in doing so the neural and classical regimes may be brought together. The input noise is Brownian mo...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 529.9KB)
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- Publication website:
- http://proceedings.mlr.press/v139/kidger21b/kidger21b.pdf
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Authors
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Pages:
- 5453-5463
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 139
- Publication date:
- 2021-07-01
- Acceptance date:
- 2021-05-08
- Event title:
- Thirty-eighth International Conference on Machine Learning (ICML 2021)
- Event location:
- Virtual event
- Event website:
- https://icml.cc/Conferences/2021
- Event start date:
- 2021-07-18T00:00:00Z
- Event end date:
- 2021-07-24T00:00:00Z
- EISSN:
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2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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1161821
- Local pid:
- pubs:1161821
- Deposit date:
- 2021-06-15
Terms of use
- Copyright holder:
- Kidger et al.
- Copyright date:
- 2021
- Rights statement:
- Copyright 2021 by the author(s).
- Notes:
- This paper was presented at the Thirty-eighth International Conference on Machine Learning (ICML 2021), 18-24 July 2021, Virtual event.
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