399 citations · 1.5k across the 18 of their papers we have counts for
4 papers · 1 filter
APMSqueeze: A Communication Efficient Adam-Preconditioned Momentum SGD Algorithm
Hanlin Tang, Shaoduo Gan, Samyam Rajbhandari +4
Adam is the important optimization algorithm to guarantee efficiency and accuracy for training many important tasks such as BERT and ImageNet. However, Adam is generally not compat…
: 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…
D: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan +2
While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from differ…