1 citations · 1 across the 1 of their papers we have counts for
3 papers
math.OC2021★ 1 cited
Error Compensated Loopless SVRG, Quartz, and SDCA for Distributed Optimization
Xun Qian, Hanze Dong, Peter Richtárik +1
The communication of gradients is a key bottleneck in distributed training of large scale machine learning models. In order to reduce the communication cost, gradient compression (…
cs.DC2019
: Decentralization Meets Error-Compensated Compression
Hanlin Tang, Xiangru Lian, Shuang Qiu +4
Communication is a key bottleneck in distributed training. Recently, an \emph{error-compensated} compression technology was particularly designed for the \emph{centralized} learnin…
cs.DC2019
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
Hanlin Tang, Xiangru Lian, Chen Yu +2
A standard approach in large scale machine learning is distributed stochastic gradient training, which requires the computation of aggregated stochastic gradients over multiple nod…