178 citations · 316 across the 4 of their papers we have counts for
4 papers
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 (…
: 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…
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…
Communication Compression for Decentralized Training
Hanlin Tang, Shaoduo Gan, Ce Zhang +2
Optimizing distributed learning systems is an art of balancing between computation and communication. There have been two lines of research that try to deal with slower networks: {…