CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
<|reference_start|>CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models: 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.<|reference_end|>
@article{xu-darme2024cabrnet,,
    title={CaBRNet, an open-source library for developing and evaluating Case-Based
  Reasoning Models},
    author={Romain Xu-Darme (LSL), Aymeric Varasse (LSL), Alban Grastien (LSL),
  Julien Girard (LSL), Zakaria Chihani (LSL)},
    journal={xAI 2024 - The 2nd World Conference on eXplainable Artificial
  Intelligence, Jul 2024, La valette, Malta. pp.TBD},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16693},
    primaryClass={cs.AI}
}