7 citations · 13 across the 4 of their papers we have counts for
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cs.DC2021
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
Da Zheng, Xiang Song, Chengru Yang +2
Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection,…
cs.LG2021
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Chengrun Yang, Ziyang Wu, Jerry Chee +2
Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-o…
stat.ML2021★ 1 cited
TenIPS: Inverse Propensity Sampling for Tensor Completion
Chengrun Yang, Lijun Ding, Ziyang Wu +1
Tensors are widely used to represent multiway arrays of data. The recovery of missing entries in a tensor has been extensively studied, generally under the assumption that entries…