Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy

6 August 2022

Papers citing "Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy"

7 / 7 papers shown

Title
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 76 2 0 14 Feb 2025
Distributed-Memory Parallel Algorithms for Sparse Matrix and Sparse Tall-and-Skinny Matrix Multiplication Isuru Ranawaka Md Taufique Hussain Charles Block Gerasimos Gerogiannis Josep Torrellas Ariful Azad 27 1 0 21 Aug 2024
EXGC: Bridging Efficiency and Explainability in Graph Condensation Junfeng Fang Xinglin Li Yongduo Sui Yuan Gao Guibin Zhang Kun Wang Xiang Wang Xiangnan He DD 53 19 0 05 Feb 2024
Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration Paul Chen Pavan Manjunath Sasindu Wijeratne Bingyi Zhang Viktor Prasanna GNN 21 8 0 04 Aug 2023
Graph Neural Network for Accurate and Low-complexity SAR ATR Bingyi Zhang Sasindu Wijeratne Rajgopal Kannan Viktor Prasanna Carl E. Busart GNN 14 4 0 11 May 2023
Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation Bingyi Zhang Viktor Prasanna GNN 19 5 0 22 Mar 2023
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,217 0 01 Sep 2014