4 citations · 8 across the 5 of their papers we have counts for
5 papers
Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier +2
This paper addresses explainable AI (XAI) through the lens of Concept Bottleneck Models (CBMs) that do not require explicit concept annotations, relying instead on concepts extract…
Explainability for Vision Foundation Models: A Survey
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven b…
Benchmarking XAI Explanations with Human-Aligned Evaluations
Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier +9
We introduce PASTA (Perceptual Assessment System for explanaTion of Artificial Intelligence), a novel human-centric framework for evaluating eXplainable AI (XAI) techniques in comp…
CLIP-QDA: An Explainable Concept Bottleneck Model
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification. Drawing inspiration from…
On Double Descent in Reinforcement Learning with LSTD and Random Features
David Brellmann, Eloïse Berthier, David Filliat +1
Temporal Difference (TD) algorithms are widely used in Deep Reinforcement Learning (RL). Their performance is heavily influenced by the size of the neural network. While in supervi…