no code implementations • 30 Jan 2024 • Noémie Cohen, Mélanie Ducoffe, Ryma Boumazouza, Christophe Gabreau, Claire Pagetti, Xavier Pucel, Audrey Galametz
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric.
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.
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.
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 • 10 Nov 2020 • Arthur Clavière, Eric Asselin, Christophe Garion, Claire Pagetti
In this paper, we propose a system-level approach for verifying the safety of neural network controlled systems, combining a continuous-time physical system with a discrete-time neural network based controller.