Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

24 October 2022

Papers citing "Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks"

6 / 6 papers shown

Title
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks Wentao Zhao Qitian Wu Chenxiao Yang Junchi Yan 17 12 0 20 Jun 2023
Cross-Task Knowledge Distillation in Multi-Task Recommendation Chenxiao Yang Junwei Pan Xiaofeng Gao Tingyu Jiang Dapeng Liu Guihai Chen 29 44 0 20 Feb 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 166 1,095 0 27 Apr 2021
Distilling Knowledge from Graph Convolutional Networks Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang 141 222 0 23 Mar 2020
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,801 0 25 Nov 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 231 3,202 0 24 Nov 2016