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Abstract
Previous work has shown that reverse differential categories give an abstract setting for gradient-based learning of functions between Euclidean spaces. However, reverse differential categories are not suited to handle gradient-based learning for functions between more general spaces such as smooth manifolds. In this paper, we propose a setting to handle this, which we call reverse tangent categories: tangent categories with an involution operation for their differential bundles.
| Original language | English |
|---|---|
| Title of host publication | CSL 2024 |
| Subtitle of host publication | 32nd EACSL Annual Conference on Computer Science Logic |
| Editors | Aniello Murano, Alexandra Silva |
| Place of Publication | Naples, Italy |
| Publisher | Dagstuhl Publishing |
| Pages | 21:1-21:21 |
| Number of pages | 21 |
| ISBN (Electronic) | 9783959773102 |
| DOIs | |
| Publication status | Published - Feb 2024 |
| Event | 32nd EACSL Annual Conference on Computer Science Logic, CSL 2024 - Naples, Italy Duration: 19 Feb 2024 → 23 Feb 2024 |
Publication series
| Name | LIPIcs - Leibniz International Proceedings in Informatics |
|---|---|
| Volume | 288 |
| ISSN (Print) | 1868-8969 |
Conference
| Conference | 32nd EACSL Annual Conference on Computer Science Logic, CSL 2024 |
|---|---|
| Country/Territory | Italy |
| City | Naples |
| Period | 19/02/24 → 23/02/24 |
Bibliographical note
© Geoffrey Cruttwell and Jean-Simon Pacaud Lemay. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Categorical Machine Learning
- Reverse Differential Categories
- Reverse Tangent Categories
- Tangent Categories
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