23 citations · 58 across the 5 of their papers we have counts for
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cs.LG2022★ 1 cited
Stochastic Gradient Descent without Full Data Shuffle
Lijie Xu, Shuang Qiu, Binhang Yuan +9
Stochastic gradient descent (SGD) is the cornerstone of modern machine learning (ML) systems. Despite its computational efficiency, SGD requires random data access that is inherent…
cs.LG2017★ 2 cited
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Heng Guo, Kaan Kara, Ce Zhang
For Markov chain Monte Carlo methods, one of the greatest discrepancies between theory and system is the scan order - while most theoretical development on the mixing time analysis…
cs.LG2016★ 15 cited
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
Hantian Zhang, Jerry Li, Kaan Kara +3
Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order…