94 citations · 119 across the 13 of their papers we have counts for
17 papers
How to design a dataset compliant with an ML-based system ODD?
Cyril Cappi, Noémie Cohen, Mélanie Ducoffe +8
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-Le…
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
Thomas Fel, Thibaut Boissin, Victor Boutin +9
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.…
Xplique: A Deep Learning Explainability Toolbox
Thomas Fel, Lucas Hervier, David Vigouroux +12
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, b…
White Paper Machine Learning in Certified Systems
Hervé Delseny, Christophe Gabreau, Adrien Gauffriau +25
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 veh…
Dataset Definition Standard (DDS)
Cyril Cappi, Camille Chapdelaine, Laurent Gardes +4
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 documen…
Ensuring Dataset Quality for Machine Learning Certification
Sylvaine Picard, Camille Chapdelaine, Cyril Cappi +4
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 stand…