18 citations · 21 across the 6 of their papers we have counts for
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cs.LG2024★ 1 cited
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective
Ming Gu, Zhuonan Zheng, Sheng Zhou +5
Graph Neural Networks (GNNs) have achieved great success but are often considered to be challenged by varying levels of homophily in graphs. Recent \textit{empirical} studies have…
cs.CV2024
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
Qingsong Lv, Jiasheng Sun, Sheng Zhou +6
To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolution…