137 citations · 175 across the 8 of their papers we have counts for
15 papers
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero +7
Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose,…
The Robustness Limits of SoTA Vision Models to Natural Variation
Mark Ibrahim, Quentin Garrido, Ari Morcos +1
Recent state-of-the-art vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classificati…
Robust Self-Supervised Learning with Lie Groups
Mark Ibrahim, Diane Bouchacourt, Ari Morcos
Deep learning has led to remarkable advances in computer vision. Even so, today's best models are brittle when presented with variations that differ even slightly from those seen d…
Addressing the Topological Defects of Disentanglement via Distributed Operators
Diane Bouchacourt, Mark Ibrahim, Stéphane Deny
A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in…
Think before you act: A simple baseline for compositional generalization
Christina Heinze-Deml, Diane Bouchacourt
Contrarily to humans who have the ability to recombine familiar expressions to create novel ones, modern neural networks struggle to do so. This has been emphasized recently with t…
Compositionality and Generalization in Emergent Languages
Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt +2
Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as \emph{compositional…