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Identifying Semantic Component for Robust Molecular Property Prediction

Identifying Semantic Component for Robust Molecular Property Prediction

8 November 2023
Zijian Li
Zunhong Xu
Ruichu Cai
Zhenhui Yang
Yuguang Yan
Zhifeng Hao
Guan-Hong Chen
Kun Zhang
ArXiv (abs)PDFHTMLGithub (4★)

Papers citing "Identifying Semantic Component for Robust Molecular Property Prediction"

37 / 37 papers shown
Title
GOOD: A Graph Out-of-Distribution Benchmark
GOOD: A Graph Out-of-Distribution Benchmark
Shurui Gui
Xiner Li
Limei Wang
Shuiwang Ji
OOD
85
123
0
16 Jun 2022
Graph Rationalization with Environment-based Augmentations
Graph Rationalization with Environment-based Augmentations
Gang Liu
Tong Zhao
Jiaxi Xu
Te Luo
Meng Jiang
OOD
87
85
0
06 Jun 2022
Exploring Chemical Space with Score-based Out-of-distribution Generation
Exploring Chemical Space with Score-based Out-of-distribution Generation
Seul Lee
Jaehyeong Jo
Sung Ju Hwang
OODD
109
80
0
06 Jun 2022
Multi-Instance Causal Representation Learning for Instance Label
  Prediction and Out-of-Distribution Generalization
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization
Weijia Zhang
Xuanhui Zhang
Hanwen Deng
Min-Ling Zhang
89
23
0
25 Feb 2022
Out-Of-Distribution Generalization on Graphs: A Survey
Out-Of-Distribution Generalization on Graphs: A Survey
Haoyang Li
Xin Eric Wang
Ziwei Zhang
Wenwu Zhu
OODCML
117
101
0
16 Feb 2022
Discovering Invariant Rationales for Graph Neural Networks
Discovering Invariant Rationales for Graph Neural Networks
Yingmin Wu
Xiang Wang
An Zhang
Xiangnan He
Tat-Seng Chua
OODAI4CE
170
234
0
30 Jan 2022
Causal Attention for Interpretable and Generalizable Graph
  Classification
Causal Attention for Interpretable and Generalizable Graph Classification
Yongduo Sui
Xiang Wang
Jiancan Wu
Min Lin
Xiangnan He
Tat-Seng Chua
CMLOOD
83
158
0
30 Dec 2021
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
Haoyang Li
Xin Eric Wang
Ziwei Zhang
Wenwu Zhu
OODDOOD
95
104
0
07 Dec 2021
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Shaohua Fan
Xiao Wang
Chuan Shi
Peng Cui
Bai Wang
CMLOODOODDAI4CE
142
88
0
20 Nov 2021
Learning Temporally Causal Latent Processes from General Temporal Data
Learning Temporally Causal Latent Processes from General Temporal Data
Weiran Yao
Yuewen Sun
Alex Ho
Changyin Sun
Kun Zhang
BDLCML
91
87
0
11 Oct 2021
3D Infomax improves GNNs for Molecular Property Prediction
3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk
Dominique Beaini
Gabriele Corso
Prudencio Tossou
Christian Dallago
Stephan Günnemann
Pietro Lio
AI4CE
92
208
0
08 Oct 2021
Motif-based Graph Self-Supervised Learning for Molecular Property
  Prediction
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Zaixin Zhang
Qi Liu
Hao Wang
Chengqiang Lu
Chee-Kong Lee
SSLAI4CE
101
259
0
03 Oct 2021
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property
  Prediction
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
Shuangli Li
Jingbo Zhou
Tong Xu
Dejing Dou
Hui Xiong
AI4CE
75
122
0
24 Sep 2021
Towards Out-Of-Distribution Generalization: A Survey
Towards Out-Of-Distribution Generalization: A Survey
Jiashuo Liu
Zheyan Shen
Yue He
Xingxuan Zhang
Renzhe Xu
Han Yu
Peng Cui
CMLOOD
155
535
0
31 Aug 2021
Geometric Deep Learning on Molecular Representations
Geometric Deep Learning on Molecular Representations
Kenneth Atz
F. Grisoni
G. Schneider
AI4CE
102
303
0
26 Jul 2021
Disentangling Identifiable Features from Noisy Data with Structured
  Nonlinear ICA
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä
Sylvain Le Corff
Luc Lehéricy
Jonathan So
Yongjie Zhu
Elisabeth Gassiat
Aapo Hyvarinen
CML
60
65
0
17 Jun 2021
Graph Domain Adaptation: A Generative View
Graph Domain Adaptation: A Generative View
Ruichu Cai
Fengzhu Wu
Zijian Li
Pengfei Wei
Lingling Yi
Kun Zhang
OOD
72
38
0
14 Jun 2021
Self-Supervised Learning with Data Augmentations Provably Isolates
  Content from Style
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Julius von Kügelgen
Yash Sharma
Luigi Gresele
Wieland Brendel
Bernhard Schölkopf
M. Besserve
Francesco Locatello
110
317
0
08 Jun 2021
HiddenCut: Simple Data Augmentation for Natural Language Understanding
  with Better Generalization
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization
Jiaao Chen
Dinghan Shen
Weizhu Chen
Diyi Yang
BDL
65
48
0
31 May 2021
Deep Stable Learning for Out-Of-Distribution Generalization
Deep Stable Learning for Out-Of-Distribution Generalization
Xingxuan Zhang
Peng Cui
Renzhe Xu
Linjun Zhou
Yue He
Zheyan Shen
OOD
84
257
0
16 Apr 2021
Uncertainty Aware Semi-Supervised Learning on Graph Data
Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao
Feng Chen
Shu Hu
Jin-Hee Cho
UQCVEDLBDL
194
139
0
24 Oct 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu
Matthias Fey
Marinka Zitnik
Yuxiao Dong
Hongyu Ren
Bowen Liu
Michele Catasta
J. Leskovec
311
2,752
0
02 May 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Andres Potapczynski
Gabriel Loaiza-Ganem
John P. Cunningham
84
30
0
19 Dec 2019
Molecular Property Prediction: A Multilevel Quantum Interactions
  Modeling Perspective
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
Chengqiang Lu
Qi Liu
Chao Wang
Zhenya Huang
Peize Lin
Lixin He
AI4CE
70
193
0
25 Jun 2019
Strategies for Pre-training Graph Neural Networks
Strategies for Pre-training Graph Neural Networks
Weihua Hu
Bowen Liu
Joseph Gomes
Marinka Zitnik
Percy Liang
Vijay S. Pande
J. Leskovec
SSLAI4CE
120
1,416
0
29 May 2019
Disentangling Factors of Variation Using Few Labels
Disentangling Factors of Variation Using Few Labels
Francesco Locatello
Michael Tschannen
Stefan Bauer
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
DRLCMLCoGe
98
124
0
03 May 2019
Simplifying Graph Convolutional Networks
Simplifying Graph Convolutional Networks
Felix Wu
Tianyi Zhang
Amauri Souza
Christopher Fifty
Tao Yu
Kilian Q. Weinberger
GNN
250
3,184
0
19 Feb 2019
Challenging Common Assumptions in the Unsupervised Learning of
  Disentangled Representations
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello
Stefan Bauer
Mario Lucic
Gunnar Rätsch
Sylvain Gelly
Bernhard Schölkopf
Olivier Bachem
OOD
143
1,473
0
29 Nov 2018
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
259
7,705
0
01 Oct 2018
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
518
1,990
0
09 Jun 2018
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CENAI
769
3,132
0
04 Jun 2018
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive
  Learning
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen
Hiroaki Sasaki
Richard Turner
OODCML
100
331
0
22 May 2018
Graph Attention Networks
Graph Attention Networks
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
484
20,265
0
30 Oct 2017
Inductive Representation Learning on Large Graphs
Inductive Representation Learning on Large Graphs
William L. Hamilton
Z. Ying
J. Leskovec
516
15,369
0
07 Jun 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
343
1,838
0
02 Mar 2017
Categorical Reparameterization with Gumbel-Softmax
Categorical Reparameterization with Gumbel-Softmax
Eric Jang
S. Gu
Ben Poole
BDL
363
5,390
0
03 Nov 2016
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
679
29,183
0
09 Sep 2016
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