4 papers
Beyond : Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures
Jules Soria, Alban Grastien, Romain Xu-Darme +3
Prototype-based neural networks are hailed as interpretable-by-design architectures. Recently, Abductive Latent Explanations (ALE) were introduced to provide formal, mathematically…
Formal Abductive Latent Explanations for Prototype-Based Networks
Jules Soria, Zakaria Chihani, Julien Girard-Satabin +3
Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes.…
The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
Michele Alberti, François Bobot, Julien Girard-Satabin +3
The formal specification and verification of machine learning programs saw remarkable progress in less than a decade, leading to a profusion of tools. However, diversity may lead t…
CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
Romain Xu-Darme, Aymeric Varasse, Alban Grastien +2
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…