Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Gardès, Laurent"'
Autor:
Cappi, Cyril, Cohen, Noémie, Ducoffe, Mélanie, Gabreau, Christophe, Gardes, Laurent, Gauffriau, Adrien, Ginestet, Jean-Brice, Mamalet, Franck, Mussot, Vincent, Pagetti, Claire, Vigouroux, David
This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we de
Externí odkaz:
http://arxiv.org/abs/2406.14027
Autor:
Fel, Thomas, Boissin, Thibaut, Boutin, Victor, Picard, Agustin, Novello, Paul, Colin, Julien, Linsley, Drew, Rousseau, Tom, Cadène, Rémi, Gardes, Laurent, Serre, Thomas
Publikováno v:
Conference on Neural Information Processing Systems (NeurIPS), 2023
Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its widespread adoption has been limited due to a reliance on
Externí odkaz:
http://arxiv.org/abs/2306.06805
Autor:
Fel, Thomas, Hervier, Lucas, Vigouroux, David, Poche, Antonin, Plakoo, Justin, Cadene, Remi, Chalvidal, Mathieu, Colin, Julien, Boissin, Thibaut, Bethune, Louis, Picard, Agustin, Nicodeme, Claire, Gardes, Laurent, Flandin, Gregory, Serre, Thomas
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, by revealing the strategy that led to a given decision, by characterizi
Externí odkaz:
http://arxiv.org/abs/2206.04394
Autor:
Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre
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. It is also an opportunity to implement
Externí odkaz:
http://arxiv.org/abs/2103.10529
Autor:
Cappi, Cyril, Chapdelaine, Camille, Gardes, Laurent, Jenn, Eric, Lefevre, Baptiste, Picard, Sylvaine, Soumarmon, Thomas
This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This document is one of the 3 documents defined in [1] to ensure the quality of da
Externí odkaz:
http://arxiv.org/abs/2101.03020
Autor:
Picard, Sylvaine, Chapdelaine, Camille, Cappi, Cyril, Gardes, Laurent, Jenn, Eric, Lefèvre, Baptiste, Soumarmon, Thomas
Publikováno v:
The 10th IEEE International Workshop on Software Certification (WoSoCer 2020)
In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML conte
Externí odkaz:
http://arxiv.org/abs/2011.01799
Autor:
Gardes, Laurent
Publikováno v:
In Econometrics and Statistics October 2022
Publikováno v:
In Econometrics and Statistics January 2020 13:137-174
Publikováno v:
Bernoulli 2013, Vol. 19, No. 5B, 2557-2589
Nonparametric regression quantiles obtained by inverting a kernel estimator of the conditional distribution of the response are long established in statistics. Attention has been, however, restricted to ordinary quantiles staying away from the tails
Externí odkaz:
http://arxiv.org/abs/1312.5123
Autor:
Gardes, Laurent, Girard, Stéphane
Publikováno v:
Electronic Journal of Statistics 6 (2012) 1715-1744
We address the estimation of conditional quantiles when the covariate is functional and when the order of the quantiles converges to one as the sample size increases. In a first time, we investigate to what extent these large conditional quantiles ca
Externí odkaz:
http://arxiv.org/abs/1107.2261