Formal description of ML models for unambiguous implementation

no code implementations • 24 Jul 2023 • Adrien Gauffriau, Claire Pagetti

Implementing deep neural networks in safety critical systems, in particular in the aeronautical domain, will require to offer adequate specification paradigms to preserve the semantics of the trained model on the final hardware platform.

LARD - Landing Approach Runway Detection -- Dataset for Vision Based Landing

1 code implementation • 5 Apr 2023 • Mélanie Ducoffe, Maxime Carrere, Léo Féliers, Adrien Gauffriau, Vincent Mussot, Claire Pagetti, Thierry Sammour

As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data.

White Paper Machine Learning in Certified Systems

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.

BIG-bench Machine Learning

Overestimation learning with guarantees

no code implementations • 26 Jan 2021 • Adrien Gauffriau, François Malgouyres, Mélanie Ducoffe

Experiments on real data show that the method makes it possible to use the surrogate function in embedded systems for which an underestimation is critical; when computing the reference function requires too many resources.