CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912045753958400 |
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| author | Xu-Darme, Romain Varasse, Aymeric Grastien, Alban Girard, Julien Chihani, Zakaria |
| author_facet | Xu-Darme, Romain Varasse, Aymeric Grastien, Alban Girard, Julien Chihani, Zakaria |
| contents | In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: https://github.com/aiser-team/cabrnet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16693 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models Xu-Darme, Romain Varasse, Aymeric Grastien, Alban Girard, Julien Chihani, Zakaria Artificial Intelligence In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: https://github.com/aiser-team/cabrnet. |
| title | CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.16693 |