11 citations · 38 across the 7 of their papers we have counts for
7 papers
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
Thomas Fel, Victor Boutin, Mazda Moayeri +5
In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs).…
Learning Functional Transduction
Mathieu Chalvidal, Thomas Serre, Rufin VanRullen
Research in machine learning has polarized into two general approaches for regression tasks: Transductive methods construct estimates directly from available data but are usually p…
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…
Meta-Reinforcement Learning with Self-Modifying Networks
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. However, these n…
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
Thomas Fel, Remi Cadene, Mathieu Chalvidal +3
We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices. Beyond modeling the individual contributions of image regions, Sobol indice…
KuraNet: Systems of Coupled Oscillators that Learn to Synchronize
Matthew Ricci, Minju Jung, Yuwei Zhang +3
Networks of coupled oscillators are some of the most studied objects in the theory of dynamical systems. Two important areas of current interest are the study of synchrony in highl…