Journal article
"Hey, that's not an ODE": faster ODE adjoints with 12 lines of code
- Abstract:
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Neural differential equations may be trained by backpropagating gradients via the adjoint method, which is another differential equation typically solved using an adaptive-step-size numerical differential equation solver. A proposed step is accepted if its error, \emph{relative to some norm}, is sufficiently small; else it is rejected, the step is shrunk, and the process is repeated. Here, we demonstrate that the particular structure of the adjoint equations makes the usual choices of norm (s...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 775.9KB)
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- Publication website:
- http://proceedings.mlr.press/v139/kidger21a.html
Authors
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 139
- Pages:
- 5443-5452
- 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:
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2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1133477
- Local pid:
- pubs:1133477
- Deposit date:
- 2021-02-18
Terms of use
- Copyright holder:
- Kidger et al.
- Copyright date:
- 2020
- 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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