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.
no code implementations • 7 Jan 2021 • Cyril Cappi, Camille Chapdelaine, Laurent Gardes, Eric Jenn, Baptiste Lefevre, Sylvaine Picard, Thomas Soumarmon
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.
no code implementations • 5 Jan 2021 • Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
The challenge in few-shot learning is that available data is not enough to capture the underlying distribution.
no code implementations • 3 Nov 2020 • Sylvaine Picard, Camille Chapdelaine, Cyril Cappi, Laurent Gardes, Eric Jenn, Baptiste Lefèvre, Thomas Soumarmon
In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems.
no code implementations • 18 Feb 2020 • Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch.
no code implementations • 20 Aug 2019 • Michel Moukari, Loïc Simon, Sylvaine Picard, Frédéric Jurie
As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics community.
no code implementations • CVPR 2019 • Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc
Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks.
no code implementations • 27 Sep 2018 • Michel Moukari, Loïc Simon, Sylvaine Picard, Frédéric Jurie
One contribution of this article is to draw attention on existing metrics developed in the forecast community, designed to evaluate both the sharpness and the calibration of predictive uncertainty.
no code implementations • 8 Jun 2018 • Michel Moukari, Sylvaine Picard, Loic Simon, Frédéric Jurie
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images.