ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2201.12872
  4. Cited By
Discovering Invariant Rationales for Graph Neural Networks

Discovering Invariant Rationales for Graph Neural Networks

30 January 2022
Yingmin Wu
Xiang Wang
An Zhang
Xiangnan He
Tat-Seng Chua
    OOD
    AI4CE
ArXivPDFHTML

Papers citing "Discovering Invariant Rationales for Graph Neural Networks"

9 / 9 papers shown
Title
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
W. Liu
Zhongyu Niu
Lang Gao
Zhiying Deng
Jun Wang
H. Wang
Ruixuan Li
6
0
0
04 May 2025
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
Md Abrar Jahin
Md. Akmol Masud
M. Mridha
Nilanjan Dey
Zeyar Aung
29
0
0
03 Nov 2024
Does Invariant Risk Minimization Capture Invariance?
Does Invariant Risk Minimization Capture Invariance?
Pritish Kamath
Akilesh Tangella
Danica J. Sutherland
Nathan Srebro
OOD
177
105
0
04 Jan 2021
Explainability in Graph Neural Networks: A Taxonomic Survey
Explainability in Graph Neural Networks: A Taxonomic Survey
Hao Yuan
Haiyang Yu
Shurui Gui
Shuiwang Ji
141
463
0
31 Dec 2020
Invariant Rationalization
Invariant Rationalization
Shiyu Chang
Yang Zhang
Mo Yu
Tommi Jaakkola
168
175
0
22 Mar 2020
Benchmarking Graph Neural Networks
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Anh Tuan Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
165
760
0
02 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David M. Krueger
Ethan Caballero
J. Jacobsen
Amy Zhang
Jonathan Binas
Dinghuai Zhang
Rémi Le Priol
Aaron Courville
OOD
194
759
0
02 Mar 2020
A causal framework for explaining the predictions of black-box
  sequence-to-sequence models
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis
Tommi Jaakkola
CML
197
189
0
06 Jul 2017
MoleculeNet: A Benchmark for Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu
Bharath Ramsundar
Evan N. Feinberg
Joseph Gomes
C. Geniesse
Aneesh S. Pappu
K. Leswing
Vijay S. Pande
OOD
138
1,486
0
02 Mar 2017
1