Journal article
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
- Abstract:
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Signatory is a library for calculating and performing functionality related to the signature and logsignature transforms. The focus is on machine learning, and as such includes features such as CPU parallelism, GPU support, and backpropagation. To our knowledge it is the first GPU-capable library for these operations. Signatory implements new features not available in previous libraries, such as efficient precomputation strategies. Furthermore, several novel algorithmic improvements are intro...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
-
(Accepted manuscript, 934.6KB)
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- Publication website:
- https://openreview.net/forum?id=lqU2cs3Zca
Authors
Bibliographic Details
- Article number:
- Poster 2220
- Publication date:
- 2021-05-03
- Acceptance date:
- 2021-01-07
- Event title:
- International Conference on Learning Representations 2021
- Event location:
- Online
- Event website:
- https://openreview.net/group?id=ICLR.cc/2021/Conference
- Event start date:
- 2021-05-03
- Event end date:
- 2021-05-07
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1082290
- Local pid:
- pubs:1082290
- Deposit date:
- 2021-02-18
Terms of use
- Copyright date:
- 2021
- Notes:
- This is the manuscript of a conference paper to be presented at the Ninth International Conference on Learning Representations (ICLR 2021), scheduled 3-7 May 2021. The accepted paper is also available via the open access platform OpenReview at https://openreview.net/forum?id=lqU2cs3Zca
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