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cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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.…