335 citations · 565 across the 11 of their papers we have counts for
3 papers · 1 filter
Augmentations vs Algorithms: What Works in Self-Supervised Learning
Warren Morningstar, Alex Bijamov, Chris Duvarney +8
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space le…
Learning Federated Representations and Recommendations with Limited Negatives
Lin Ning, Karan Singhal, Ellie X. Zhou +1
Deep retrieval models are widely used for learning entity representations and recommendations. Federated learning provides a privacy-preserving way to train these models without re…
Federated Reconstruction: Partially Local Federated Learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett +3
Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client hetero…