Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization

1 code implementation • 11 Jun 2023 • Thomas Fel, Thibaut Boissin, Victor Boutin, Agustin Picard, Paul Novello, Julien Colin, Drew Linsley, Tom Rousseau, Rémi Cadène, Laurent Gardes, Thomas Serre

However, its widespread adoption has been limited due to a reliance on tricks to generate interpretable images, and corresponding challenges in scaling it to deeper neural networks.

Xplique: A Deep Learning Explainability Toolbox

1 code implementation • 9 Jun 2022 • Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Bethune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin, Thomas Serre

Today's most advanced machine-learning models are hardly scrutable.

Explainable artificial intelligence

Dataset Definition Standard (DDS)

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.

BIG-bench Machine Learning

Ensuring Dataset Quality for Machine Learning Certification

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.

BIG-bench Machine Learning Management