39 citations · 84 across the 12 of their papers we have counts for
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cs.LG2023
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks
Rui Wang, Zhiming Zhou, Tao Zhang +4
Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the…
cs.LG2021★ 1 cited
CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation
Enda Yu, Dezun Dong, Yemao Xu +2
Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combining with parallel c…
cs.LG2020★ 1 cited
OD-SGD: One-step Delay Stochastic Gradient Descent for Distributed Training
Yemao Xu, Dezun Dong, Weixia Xu +1
The training of modern deep learning neural network calls for large amounts of computation, which is often provided by GPUs or other specific accelerators. To scale out to achieve…