198 citations · 480 across the 36 of their papers we have counts for
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stat.ML2018★ 13 cited
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Tim Tsz-Kit Lau, Jinshan Zeng, Baoyuan Wu +1
Training deep neural networks (DNNs) efficiently is a challenge due to the associated highly nonconvex optimization. The backpropagation (backprop) algorithm has long been the most…
math.OC2018★ 32 cited
Global Convergence of Block Coordinate Descent in Deep Learning
Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin +1
Deep learning has aroused extensive attention due to its great empirical success. The efficiency of the block coordinate descent (BCD) methods has been recently demonstrated in dee…
math.OC2018★ 2 cited
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
Jinshan Zeng, Ke Ma, Yuan Yao
There is a recent surge of interest in nonconvex reformulations via low-rank factorization for stochastic convex semidefinite optimization problem in the purpose of efficiency and…