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Neural controlled differential equations for irregular time series

Abstract:

Neural ordinary differential equations are an attractive option for modelling temporal dynamics. However, a fundamental issue is that the solution to an ordinary differential equation is determined by its initial condition, and there is no mechanism for adjusting the trajectory based on subsequent observations. Here, we demonstrate how this may be resolved through the well-understood mathematics of controlled differential equations. The resulting neural controlled differential equation model ...

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Publication status:
Published
Peer review status:
Peer reviewed

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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:
Neural Information Processing Systems Foundation, Inc. Publisher's website
Host title:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Publication date:
2020-12-10
Acceptance date:
2020-09-26
Event title:
34th Conference on Neural Information Processing Systems (NeurIPS)
Event location:
Virtual event
Event website:
https://neurips.cc/Conferences/2020
Event start date:
2020-12-06T00:00:00Z
Event end date:
2020-12-12T00:00:00Z
Language:
English
Keywords:
Pubs id:
1150059
Local pid:
pubs:1150059
Deposit date:
2021-06-15

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