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Jian Tang (唐建)
Jian Tang (唐建)
Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI Chair
Verified email at hec.ca - Homepage
Title
Cited by
Cited by
Year
Line: Large-scale information network embedding
J Tang, M Qu, M Wang, M Zhang, J Yan, Q Mei
Proceedings of the 24th international conference on world wide web, 1067-1077, 2015
58182015
Rotate: Knowledge graph embedding by relational rotation in complex space
Z Sun, ZH Deng, JY Nie, J Tang
ICLR 2019, 2019
18622019
Pte: Predictive text embedding through large-scale heterogeneous text networks
J Tang, M Qu, Q Mei
Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015
8812015
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
FY Sun, J Hoffmann, V Verma, J Tang
ICLR 2020 (Spotlight), 2020
7242020
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
W Song, C Shi, Z Xiao, Z Duan, Y Xu, M Zhang, J Tang
CIKM 2019, 2019
6062019
Deepinf: Social influence prediction with deep learning
J Qiu, J Tang, H Ma, Y Dong, K Wang, J Tang
Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018
4822018
Visualizing large-scale and high-dimensional data
J Tang, J Liu, M Zhang, Q Mei
Proceedings of the 25th international conference on world wide web, 287-297, 2016
4672016
KEPLER: A unified model for knowledge embedding and pre-trained language representation
X Wang, T Gao, Z Zhu, Z Zhang, Z Liu, J Li, J Tang
Transactions of the Association for Computational Linguistics 9, 176-194, 2021
4592021
Artificial intelligence in COVID-19 drug repurposing
Y Zhou, F Wang, J Tang, R Nussinov, F Cheng
The Lancet Digital Health 2 (12), e667-e676, 2020
4142020
Session-based social recommendation via dynamic graph attention networks
W Song, Z Xiao, Y Wang, L Charlin, M Zhang, J Tang
Proceedings of the Twelfth ACM international conference on web search and …, 2019
3882019
Understanding the limiting factors of topic modeling via posterior contraction analysis
J Tang, Z Meng, X Nguyen, Q Mei, M Zhang
International conference on machine learning, 190-198, 2014
3482014
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
C Shi, M Xu, Z Zhu, W Zhang, M Zhang, J Tang
ICLR 2020, 2020
3172020
GMNN: Graph Markov Neural Networks
M Qu, Y Bengio, J Tang
ICML 2019, 2019
2982019
Adversarial network embedding
Q Dai, Q Li, J Tang, D Wang
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
2432018
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang
ICLR 2022 Oral, 2022
2322022
Utilizing graph machine learning within drug discovery and development
T Gaudelet, B Day, AR Jamasb, J Soman, C Regep, G Liu, JBR Hayter, ...
Briefings in bioinformatics 22 (6), bbab159, 2021
1832021
An attention-based collaboration framework for multi-view network representation learning
M Qu, J Tang, J Shang, X Ren, M Zhang, J Han
Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017
1792017
Probabilistic Logic Neural Networks for Reasoning
M Qu, J Tang
NeurIPS'19, 2019
1612019
Pre-training Molecular Graph Representation with 3D Geometry
S Liu, H Wang, W Liu, J Lasenby, H Guo, J Tang
ICLR 2022, 2022
1522022
Neural bellman-ford networks: A general graph neural network framework for link prediction
Z Zhu, Z Zhang, LP Xhonneux, J Tang
Advances in Neural Information Processing Systems 34, 29476-29490, 2021
1442021
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