4 citations · 4 across the 1 of their papers we have counts for
3 papers
cs.LG2022★ 4 cited
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Xin Liu, Mingyu Yan, Lei Deng +5
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urge…
cs.LG2021
Sampling methods for efficient training of graph convolutional networks: A survey
Xin Liu, Mingyu Yan, Lei Deng +3
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GC…
cs.CV2019
VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks
Shuang Gao, Xin Liu, Lung-Sheng Chien +2
Improving weight sparsity is a common strategy for producing light-weight deep neural networks. However, pruning models with residual learning is more challenging. In this paper, w…