Conference item
Neural rough differential equations for long time series
- Abstract:
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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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(Version of record, 533.2KB)
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- Publication website:
- http://proceedings.mlr.press/v139/morrill21b.html
Authors
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 139
- Pages:
- 7829-7838
- 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:
- 2021-07-18
- Event end date:
- 2021-07-24
- ISSN:
-
2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1136516
- Local pid:
- pubs:1136516
- Deposit date:
- 2021-06-15
Terms of use
- Copyright holder:
- Morrill 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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