no code implementations • 5 Feb 2024 • Armand Foucault, Franck Mamalet, François Malgouyres
Orthogonal recurrent neural networks (ORNNs) are an appealing option for learning tasks involving time series with long-term dependencies, thanks to their simplicity and computational stability.
1 code implementation • 25 May 2023 • Louis Bethune, Thomas Massena, Thibaut Boissin, Yannick Prudent, Corentin Friedrich, Franck Mamalet, Aurelien Bellet, Mathieu Serrurier, David Vigouroux
To provide sensitivity bounds and bypass the drawbacks of the clipping process, we propose to rely on Lipschitz constrained networks.
no code implementations • NeurIPS 2023 • Mathieu Serrurier, Franck Mamalet, Thomas Fel, Louis Béthune, Thibaut Boissin
Input gradients have a pivotal role in a variety of applications, including adversarial attack algorithms for evaluating model robustness, explainable AI techniques for generating Saliency Maps, and counterfactual explanations. However, Saliency Maps generated by traditional neural networks are often noisy and provide limited insights.
no code implementations • 12 Aug 2021 • El Mehdi Achour, François Malgouyres, Franck Mamalet
Imposing orthogonality on the layers of neural networks is known to facilitate the learning by limiting the exploding/vanishing of the gradient; decorrelate the features; improve the robustness.
1 code implementation • 11 Apr 2021 • Louis Béthune, Thibaut Boissin, Mathieu Serrurier, Franck Mamalet, Corentin Friedrich, Alberto González-Sanz
However they remain commonly considered as less accurate, and their properties in learning are still not fully understood.
2 code implementations • 18 Mar 2021 • Hervé Delseny, Christophe Gabreau, Adrien Gauffriau, Bernard Beaudouin, Ludovic Ponsolle, Lucian Alecu, Hugues Bonnin, Brice Beltran, Didier Duchel, Jean-Brice Ginestet, Alexandre Hervieu, Ghilaine Martinez, Sylvain Pasquet, Kevin Delmas, Claire Pagetti, Jean-Marc Gabriel, Camille Chapdelaine, Sylvaine Picard, Mathieu Damour, Cyril Cappi, Laurent Gardès, Florence De Grancey, Eric Jenn, Baptiste Lefevre, Gregory Flandin, Sébastien Gerchinovitz, Franck Mamalet, Alexandre Albore
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc.
1 code implementation • CVPR 2021 • Mathieu Serrurier, Franck Mamalet, Alberto González-Sanz, Thibaut Boissin, Jean-Michel Loubes, Eustasio del Barrio
This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound.