44 citations · 199 across the 22 of their papers we have counts for
4 papers · 1 filter
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Ran Liu, Mehdi Azabou, Max Dabagia +5
Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, f…
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski +13
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this…
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu +10
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient…
Large-Scale Representation Learning on Graphs via Bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar +5
Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-…