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Confidence May Cheat: Self-Training on Graph Neural Networks under
  Distribution Shift

Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift

27 January 2022
Hongrui Liu
Binbin Hu
Xiao Wang
Chuan Shi
Qing Cui
Jun Zhou
ArXivPDFHTML

Papers citing "Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift"

26 / 26 papers shown
Title
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang
Shuhan Liu
Song Wang
Weili Shi
Chen Chen
Pan Li
Sheng Li
Jundong Li
Kaize Ding
OOD
118
4
0
25 Oct 2024
Uncertainty in Graph Neural Networks: A Survey
Uncertainty in Graph Neural Networks: A Survey
Fangxin Wang
Yuqing Liu
Kay Liu
Yibo Wang
Sourav Medya
Philip S. Yu
AI4CE
108
10
0
11 Mar 2024
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence
  Calibration
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
Xiao Wang
Hongrui Liu
Chuan Shi
Cheng Yang
UQCV
155
119
0
29 Sep 2021
SLADE: A Self-Training Framework For Distance Metric Learning
SLADE: A Self-Training Framework For Distance Metric Learning
Jiali Duan
Yen-Liang Lin
Son N. Tran
Larry S. Davis
C.-C. Jay Kuo
50
11
0
20 Nov 2020
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh
Ehsan Hajiramezanali
Shahin Boluki
Mingyuan Zhou
N. Duffield
Krishna R. Narayanan
Xiaoning Qian
BDL
46
118
0
07 Jun 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Antonin Berthon
Bo Han
Gang Niu
Tongliang Liu
Masashi Sugiyama
NoLa
84
108
0
11 Jan 2020
Revisiting Self-Training for Neural Sequence Generation
Revisiting Self-Training for Neural Sequence Generation
Junxian He
Jiatao Gu
Jiajun Shen
MarcÁurelio Ranzato
SSL
LRM
273
272
0
30 Sep 2019
DropEdge: Towards Deep Graph Convolutional Networks on Node
  Classification
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Yu Rong
Wenbing Huang
Tingyang Xu
Junzhou Huang
107
1,339
0
25 Jul 2019
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on
  Graphs with Few Labels
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Ke Sun
Zhouchen Lin
Zhanxing Zhu
SSL
93
276
0
28 Feb 2019
Simplifying Graph Convolutional Networks
Simplifying Graph Convolutional Networks
Felix Wu
Tianyi Zhang
Amauri Souza
Christopher Fifty
Tao Yu
Kilian Q. Weinberger
GNN
226
3,172
0
19 Feb 2019
A Comprehensive Survey on Graph Neural Networks
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu
Shirui Pan
Fengwen Chen
Guodong Long
Chengqi Zhang
Philip S. Yu
FaML
GNN
AI4TS
AI4CE
750
8,517
0
03 Jan 2019
Graph Neural Networks: A Review of Methods and Applications
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou
Ganqu Cui
Shengding Hu
Zhengyan Zhang
Cheng Yang
Zhiyuan Liu
Lifeng Wang
Changcheng Li
Maosong Sun
AI4CE
GNN
1.1K
5,515
0
20 Dec 2018
Deep Learning on Graphs: A Survey
Deep Learning on Graphs: A Survey
Ziwei Zhang
Peng Cui
Wenwu Zhu
GNN
149
1,333
0
11 Dec 2018
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Klicpera
Aleksandar Bojchevski
Stephan Günnemann
GNN
214
1,685
0
14 Oct 2018
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
236
7,638
0
01 Oct 2018
Deep Clustering for Unsupervised Learning of Visual Features
Deep Clustering for Unsupervised Learning of Visual Features
Mathilde Caron
Piotr Bojanowski
Armand Joulin
Matthijs Douze
SSL
88
1,894
0
15 Jul 2018
Deeper Insights into Graph Convolutional Networks for Semi-Supervised
  Learning
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li
Zhichao Han
Xiao-Ming Wu
GNN
SSL
182
2,825
0
22 Jan 2018
Graph Attention Networks
Graph Attention Networks
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
466
20,124
0
30 Oct 2017
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via
  Ranking
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Aleksandar Bojchevski
Stephan Günnemann
BDL
83
642
0
12 Jul 2017
Inductive Representation Learning on Large Graphs
Inductive Representation Learning on Large Graphs
William L. Hamilton
Z. Ying
J. Leskovec
487
15,232
0
07 Jun 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction
  Approach
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini
A. Rozza
A. Menon
Richard Nock
Zhuang Li
NoLa
90
1,452
0
13 Sep 2016
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNN
SSL
612
29,032
0
09 Sep 2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral
  Filtering
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
M. Defferrard
Xavier Bresson
P. Vandergheynst
GNN
337
7,648
0
30 Jun 2016
Black-box $α$-divergence Minimization
Black-box ααα-divergence Minimization
José Miguel Hernández-Lobato
Yingzhen Li
Mark Rowland
Daniel Hernández-Lobato
T. Bui
Richard Turner
107
140
0
10 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
815
9,302
0
06 Jun 2015
Spectral Networks and Locally Connected Networks on Graphs
Spectral Networks and Locally Connected Networks on Graphs
Joan Bruna
Wojciech Zaremba
Arthur Szlam
Yann LeCun
GNN
215
4,874
0
21 Dec 2013
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