10 citations · 21 across the 7 of their papers we have counts for
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cs.DC2019★ 10 cited
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
Chengjie Li, Ruixuan Li, Haozhao Wang +4
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models. As data are dist…
cs.DC2019★ 3 cited
Heterogeneity-aware Gradient Coding for Straggler Tolerance
Haozhao Wang, Song Guo, Bin Tang +2
Gradient descent algorithms are widely used in machine learning. In order to deal with huge volume of data, we consider the implementation of gradient descent algorithms in a distr…
cs.LG2019★ 1 cited
AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Neural Networks
Jinrong Guo, Wantao Liu, Wang Wang +4
Typically, Ultra-deep neural network(UDNN) tends to yield high-quality model, but its training process is usually resource intensive and time-consuming. Modern GPU's scarce DRAM ca…