11 citations · 28 across the 9 of their papers we have counts for
5 papers · 1 filter
CRAFT: Concept Recursive Activation FacTorization for Explainability
Thomas Fel, Agustin Picard, Louis Bethune +5
Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explaina…
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning
Thomas Mullor, David Vigouroux, Louis Bethune
Quantum walks on binary trees are used in many quantum algorithms to achieve important speedup over classical algorithms. The formulation of this kind of algorithms as quantum circ…
Xplique: A Deep Learning Explainability Toolbox
Thomas Fel, Lucas Hervier, David Vigouroux +12
Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, b…
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
Paul Novello, Thomas Fel, David Vigouroux
This paper presents a new efficient black-box attribution method based on Hilbert-Schmidt Independence Criterion (HSIC), a dependence measure based on Reproducing Kernel Hilbert Sp…
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
Thomas Fel, Melanie Ducoffe, David Vigouroux +4
A variety of methods have been proposed to try to explain how deep neural networks make their decisions. Key to those approaches is the need to sample the pixel space efficiently i…