908 citations · 2.4k across the 149 of their papers we have counts for
Showing 2018 · cs.LGShow all
2 papers · 2 filters
cs.LG2018★ 13 cited
The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson +3
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of comm…
cs.LG2018★ 172 cited
Byzantine Stochastic Gradient Descent
Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the machines which allegedly compute stochastic gradients every it…