Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural
Networks

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

6 February 2023

Shuai Zhang

Papers citing "Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks"

7 / 7 papers shown

Title
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers Hongkang Li Yihua Zhang Shuai Zhang Hao Wu Sijia Liu Pin-Yu Chen MoMe 69 3 0 15 Apr 2025
IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks Junchao Lin Zenan Ling Zhanbo Feng Feng Zhou Jingwen Xu Feng Zhou Tianqi Hou Zhenyu Liao Robert C. Qiu GNN AI4CE 52 0 0 11 Oct 2024
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity Hongkang Li Hao Wu Sijia Liu Pin-Yu Chen ViT MLT 37 57 0 12 Feb 2023
Distilling Knowledge from Graph Convolutional Networks Yiding Yang Jiayan Qiu Xiuming Zhang Dacheng Tao Xinchao Wang 160 226 0 23 Mar 2020
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud Weijing Shi Ragunathan R. Rajkumar 3DPC 165 737 0 02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 267 1,945 0 09 Jun 2018
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,743 0 26 Sep 2016