CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models - CEA - Commissariat à l’énergie atomique et aux énergies alternatives
Communication Dans Un Congrès Année : 2024

CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

Résumé

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.
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Dates et versions

cea-04688217 , version 1 (04-09-2024)
cea-04688217 , version 2 (24-09-2024)

Identifiants

Citer

Romain Xu-Darme, Aymeric Varasse, Alban Grastien, Julien Girard, Zakaria Chihani. CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models. xAI 2024 - The 2nd World Conference on eXplainable Artificial Intelligence, Jul 2024, La valette, Malta. pp.TBD. ⟨cea-04688217v2⟩
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