7 citations · 7 across the 4 of their papers we have counts for
4 papers
-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Vlad Sobal, Mark Ibrahim, Randall Balestriero +5
Learning good representations involves capturing the diverse ways in which data samples relate. Contrastive loss - an objective matching related samples - underlies methods from se…
Learning Associative Memories with Gradient Descent
Vivien Cabannes, Berfin Simsek, Alberto Bietti
This work focuses on the training dynamics of one associative memory module storing outer products of token embeddings. We reduce this problem to the study of a system of particles…
Mode Estimation with Partial Feedback
Charles Arnal, Vivien Cabannes, Vianney Perchet
The combination of lightly supervised pre-training and online fine-tuning has played a key role in recent AI developments. These new learning pipelines call for new theoretical fra…
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien Cabannes, Bobak T. Kiani, Randall Balestriero +2
Self-supervised learning (SSL) has emerged as a powerful framework to learn representations from raw data without supervision. Yet in practice, engineers face issues such as instab…