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.
1 code implementation • 26 Jan 2023 • Louis Bethune, Paul Novello, Thibaut Boissin, Guillaume Coiffier, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis
The distance to the support can be interpreted as a normality score, and its approximation using 1-Lipschitz neural networks provides robustness bounds against $l2$ adversarial attacks, an under-explored weakness of deep learning-based OCC algorithms.
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 • 24 Mar 2022 • Mohamed Chafik Bakkay, Mathieu Serrurier, Valentin Kivachuk Burda, Florian Dupuy, Naty Citlali Cabrera-Gutierrez, Michael Zamo, Maud-Alix Mader, Olivier Mestre, Guillaume Oller, Jean-Christophe Jouhaud, Laurent Terray
Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management.
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.
no code implementations • 17 Jul 2020 • Jérôme Bolte, Lilian Glaudin, Edouard Pauwels, Mathieu Serrurier
We present a new algorithm to solve min-max or min-min problems out of the convex world.
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.
no code implementations • 15 Apr 2020 • Gilles Richard, Mathieu Serrurier
Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc.
no code implementations • 21 Feb 2020 • Achraf Bennis, Sandrine Mouysset, Mathieu Serrurier
We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features.
no code implementations • 13 Dec 2019 • Zied Bouraoui, Antoine Cornuéjols, Thierry Denœux, Sébastien Destercke, Didier Dubois, Romain Guillaume, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Christel Vrain
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
1 code implementation • ECCV 2020 • Eduardo Hugo Sanchez, Mathieu Serrurier, Mathias Ortner
In this paper, we investigate the problem of learning disentangled representations.
no code implementations • 21 Mar 2019 • Eduardo Sanchez, Mathieu Serrurier, Mathias Ortner
In this paper, we investigate how to learn a suitable representation of satellite image time series in an unsupervised manner by leveraging large amounts of unlabeled data.
no code implementations • 24 Feb 2014 • Mohammad Ghasemi Hamed, Mathieu Serrurier, Nicolas Durand
This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression without the homoscedasticity assumption.