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2020 – today
- 2024
[c10]Louis Béthune, Thomas Massena, Thibaut Boissin, Aurélien Bellet, Franck Mamalet, Yannick Prudent, Corentin Friedrich, Mathieu Serrurier, David Vigouroux:
DP-SGD Without Clipping: The Lipschitz Neural Network Way. ICLR 2024- [c9]Agustin Picard, Lucas Hervier, Thomas Fel, David Vigouroux:
Influenciæ: A Library for Tracing the Influence Back to the Data-Points. xAI (4) 2024: 193-204 - [i10]Cyril Cappi, Noémie Cohen, Mélanie Ducoffe, Christophe Gabreau, Laurent Gardes, Adrien Gauffriau, Jean-Brice Ginestet, Franck Mamalet, Vincent Mussot, Claire Pagetti, David Vigouroux:
How to design a dataset compliant with an ML-based system ODD? CoRR abs/2406.14027 (2024) - 2023
- [c8]Mouhcine Mendil, Luca Mossina, David Vigouroux:
PUNCC: a Python Library for Predictive Uncertainty Calibration and Conformalization. COPA 2023: 582-601 - [c7]Thomas Fel, Agustin Martin Picard, Louis Béthune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, Thomas Serre:
CRAFT: Concept Recursive Activation FacTorization for Explainability. CVPR 2023: 2711-2721 - [c6]Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodème, Thomas Serre:
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis. CVPR 2023: 16153-16163 - [i9]Louis Béthune, Thomas Massena, Thibaut Boissin, Yannick Prudent, Corentin Friedrich, Franck Mamalet, Aurélien Bellet, Mathieu Serrurier, David Vigouroux:
DP-SGD Without Clipping: The Lipschitz Neural Network Way. CoRR abs/2305.16202 (2023) - 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 Poché, Justin Plakoo, Rémi Cadène, Mathieu Chalvidal, Julien Colin
, Thibaut Boissin, Louis Béthune, Agustin Martin 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 Martin 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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