249 citations · 699 across the 39 of their papers we have counts for
4 papers · 1 filter
Vectorizing string entries for data processing on tables: when are larger language models better?
Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim +1
There are increasingly efficient data processing pipelines that work on vectors of numbers, for instance most machine learning models, or vector databases for fast similarity searc…
Can Forward Gradient Match Backpropagation?
Louis Fournier, Stéphane Rivaud, Eugene Belilovsky +2
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding…
: Accelerating Asynchronous Communication in Decentralized Deep Learning
Adel Nabli, Eugene Belilovsky, Edouard Oyallon
Distributed training of Deep Learning models has been critical to many recent successes in the field. Current standard methods primarily rely on synchronous centralized algorithms…
Guiding The Last Layer in Federated Learning with Pre-Trained Models
Gwen Legate, Nicolas Bernier, Lucas Caccia +2
Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data. Recent works have begun to consider the effe…