no code implementations • 12 Apr 2023 • Léo Andéol, Thomas Fel, Florence De Grancey, Luca Mossina
Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty.
no code implementations • 26 Jan 2023 • Léo Andéol, Thomas Fel, Florence De Grancey, Luca Mossina
We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals.
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.