137 citations · 348 across the 31 of their papers we have counts for
10 papers · 1 filter
Reassessing the Validity of Spurious Correlations Benchmarks
Samuel J. Bell, Diane Bouchacourt, Levent Sagun
Neural networks can fail when the data contains spurious correlations. To understand this phenomenon, researchers have proposed numerous spurious correlations benchmarks upon which…
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
Ouail Kitouni, Niklas Nolte, Diane Bouchacourt +3
Today's best language models still struggle with hallucinations: factually incorrect generations, which impede their ability to reliably retrieve information seen during training.…
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood, Julius von Kügelgen, Linus Ericsson +4
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, with downstream tasks generally unknown…
Discovering environments with XRM
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim +3
Environment annotations are essential for the success of many out-of-distribution (OOD) generalization methods. Unfortunately, these are costly to obtain and often limited by human…
Disentanglement of Correlated Factors via Hausdorff Factorized Support
Karsten Roth, Mark Ibrahim, Zeynep Akata +2
A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning…
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…
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
Diane Bouchacourt, Ludovic Denoyer
Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and sim…
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control. Specifically, we want to use minim…