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Neural SDEs as infinite-dimensional GANs

Abstract:

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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Publication website:
http://proceedings.mlr.press/v139/kidger21b/kidger21b.pdf

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
St Hilda's College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
ORCID:
0000-0002-9972-2809
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:
2640-3498
Language:
English
Keywords:
Pubs id:
1161821
Local pid:
pubs:1161821
Deposit date:
2021-06-15

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