10 citations · 19 across the 11 of their papers we have counts for
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cs.LG2024★ 7 cited
FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
Zeyu Zhu, Peisong Wang, Qinghao Hu +3
Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution…
cs.LG2023★ 1 cited
: Aggregation-Aware Quantization for Graph Neural Networks
Zeyu Zhu, Fanrong Li, Zitao Mo +5
As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). Wh…
cs.LG2022★ 10 cited
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
Nanyang Ye, Jingbiao Mei, Zhicheng Fang +4
To deploy deep learning algorithms on resource-limited scenarios, an emerging device-resistive random access memory (ReRAM) has been regarded as promising via analog computing. How…