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

Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis

1 code implementation • NeurIPS 2021 • Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre

We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices.

KuraNet: Systems of Coupled Oscillators that Learn to Synchronize

1 code implementation • 6 May 2021 • Matthew Ricci, Minju Jung, Yuwei Zhang, Mathieu Chalvidal, Aneri Soni, Thomas Serre

Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions.

Go with the Flow: Adaptive Control for Neural ODEs

no code implementations • ICLR 2021 • Mathieu Chalvidal, Matthew Ricci, Rufin VanRullen, Thomas Serre

Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations.

Image Reconstruction Representation Learning

Meta-Reinforcement Learning with Self-Modifying Networks

no code implementations • 4 Feb 2022 • Mathieu Chalvidal, Thomas Serre, Rufin VanRullen

Deep Reinforcement Learning has demonstrated the potential of neural networks tuned with gradient descent for solving complex tasks in well-delimited environments.

Meta Reinforcement Learning One-Shot Learning +2