17 citations · 54 across the 14 of their papers we have counts for
Showing 2019 · stat.MLShow all
3 papers · 2 filters
stat.ML2019
ADASS: Adaptive Sample Selection for Training Acceleration
Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Stochastic gradient decent~(SGD) and its variants, including some accelerated variants, have become popular for training in machine learning. However, in all existing SGD and its v…
stat.ML2019★ 1 cited
On the Convergence of Memory-Based Distributed SGD
Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Distributed stochastic gradient descent~(DSGD) has been widely used for optimizing large-scale machine learning models, including both convex and non-convex models. With the rapid…
stat.ML2019★ 1 cited
Global Momentum Compression for Sparse Communication in Distributed Learning
Chang-Wei Shi, Shen-Yi Zhao, Yin-Peng Xie +2
With the rapid growth of data, distributed momentum stochastic gradient descent~(DMSGD) has been widely used in distributed learning, especially for training large-scale deep model…