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David Vigouroux
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2020 – today
- 2022
- [c5]Thomas Mullor, David Vigouroux, Louis Béthune:
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning. SEC 2022: 436-443 - [c4]Paul Novello, Thomas Fel, David Vigouroux:
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. NeurIPS 2022 - [c3]Florence De Grancey, Jean-Luc Adam, Lucian Alecu, Sébastien Gerchinovitz, Franck Mamalet, David Vigouroux:
Object Detection with Probabilistic Guarantees: A Conformal Prediction Approach. SAFECOMP Workshops 2022: 316-329 - [c2]Thomas Fel, David Vigouroux, Rémi Cadène, Thomas Serre:
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks. WACV 2022: 1565-1575 - [i8]Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodeme, Thomas Serre:
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis. CoRR abs/2202.07728 (2022) - [i7]Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Rémi Cadène, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Béthune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Grégory Flandin, Thomas Serre:
Xplique: A Deep Learning Explainability Toolbox. CoRR abs/2206.04394 (2022) - [i6]Paul Novello, Thomas Fel, David Vigouroux:
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. CoRR abs/2206.06219 (2022) - [i5]Thomas Mullor, David Vigouroux, Louis Béthune:
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning. CoRR abs/2210.06784 (2022) - [i4]Thomas Fel, Agustin Picard, Louis Béthune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, Thomas Serre:
CRAFT: Concept Recursive Activation FacTorization for Explainability. CoRR abs/2211.10154 (2022) - 2021
- [c1]Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre:
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis. NeurIPS 2021: 26005-26014 - [i3]Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre:
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis. CoRR abs/2111.04138 (2021) - 2020
- [i2]Thomas Fel, David Vigouroux:
Representativity and Consistency Measures for Deep Neural Network Explanations. CoRR abs/2009.04521 (2020)
2010 – 2019
- 2018
- [i1]David Vigouroux, Sylvain Picard:
FUNN: Flexible Unsupervised Neural Network. CoRR abs/1811.01749 (2018)
Coauthor Index

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