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Neural rough differential equations for long time series

Abstract:

Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions of potentially irregular time series. Existing methods for computing the forward pass of a Neural CDE involve embedding the incoming time series into path space, often via interpolation, and using evaluations of this path to drive the hidden state. Here, we use rough path theo...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
http://proceedings.mlr.press/v139/morrill21b.html

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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
Oxford college:
St Hilda's College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
Publisher:
Journal of Machine Learning Research Publisher's website
Pages:
7829-7838
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
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1136516
Local pid:
pubs:1136516
Deposit date:
2021-06-15

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