Geometrically Principled Connections in Graph Neural Networks

6 April 2020

Papers citing "Geometrically Principled Connections in Graph Neural Networks"

8 / 8 papers shown

Title
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets Tianjin Huang Tianlong Chen Meng Fang Vlado Menkovski Jiaxu Zhao ... Yulong Pei D. Mocanu Zhangyang Wang Mykola Pechenizkiy Shiwei Liu GNN 34 14 0 28 Nov 2022
On Provable Benefits of Depth in Training Graph Convolutional Networks Weilin Cong M. Ramezani M. Mahdavi 19 73 0 28 Oct 2021
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Tianlong Chen Kaixiong Zhou Keyu Duan Wenqing Zheng Peihao Wang Xia Hu Zhangyang Wang AAML GNN 19 61 0 24 Aug 2021
Training Graph Neural Networks with 1000 Layers Guohao Li Matthias Muller Bernard Ghanem V. Koltun GNN AI4CE 34 235 0 14 Jun 2021
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation Mehdi Bahri Eimear O' Sullivan Shunwang Gong Feng Liu Xiaoming Liu M. Bronstein S. Zafeiriou CVBM 3DH 15 16 0 16 Dec 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 229 1,941 0 09 Jun 2018
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 1,811 0 25 Nov 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 236 3,234 0 24 Nov 2016