Jian Tang (唐建）个人学术档案

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Meng QuQuebec AI Institute (Mila)在 umontreal.ca 的电子邮件经过验证
Ming Zhang（张铭）Professor,Peking University在 pku.edu.cn 的电子邮件经过验证
Qiaozhu MeiProfessor, University of Michigan在 umich.edu 的电子邮件经过验证
Chence ShiQuebec AI Institute (Mila)在 umontreal.ca 的电子邮件经过验证
Minkai XuStanford University在 cs.stanford.edu 的电子邮件经过验证
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFAR在 umontreal.ca 的电子邮件经过验证
Zhaocheng ZhuMila - Quebec AI Institute在 umontreal.ca 的电子邮件经过验证
Shengchao LiuUC Berkeley & Caltech在 berkeley.edu 的电子邮件经过验证
Zhi-Hong DengSchool of Intelligence Science and Technology, Peking University在 pku.edu.cn 的电子邮件经过验证
Weiping Song (宋卫平)Peking University在 pku.edu.cn 的电子邮件经过验证
Zhiyuan LiuAssociate Professor, Tsinghua University在 tsinghua.edu.cn 的电子邮件经过验证
Andreea DeacIsomorphic Labs在 google.com 的电子邮件经过验证
Feixiong ChengCleveland Clinic在 ccf.org 的电子邮件经过验证
Laurent CharlinAssociate Professor, HEC Montréal & Mila, Canada CIFAR AI Chair在 hec.ca 的电子邮件经过验证
XuanLong NguyenUniversity of Michigan在 umich.edu 的电子邮件经过验证
Connor W. ColeyDepartment of Chemical Engineering, MIT在 mit.edu 的电子邮件经过验证
Rafael Gómez-BombarelliAssociate Professor, MIT DMSE在 mit.edu 的电子邮件经过验证
Xuanzhe LiuBoya Distinguished Professor of Computer Science, Peking University, ACM Distinguished Scientist在 pku.edu.cn 的电子邮件经过验证
Bolanle OjokohDepartment of Information Systems, Federal University of Technology, Akure, Nigeria在 futa.edu.ng 的电子邮件经过验证
Jian PengHelixon在 helixon.com 的电子邮件经过验证

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Jian Tang (唐建）

Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI Chair

在 hec.ca 的电子邮件经过验证 - 首页

Geometric deep learning AI for molecule/protein design knowledge graphs deep generative models


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	6256	2015
Rotate: Knowledge graph embedding by relational rotation in complex space Z Sun, ZH Deng, JY Nie, J Tang ICLR 2019, 2019	2303	2019
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	942	2015
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	920	2020
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	727	2019
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	616	2021
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	538	2018
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	498	2016
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	479	2020
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	455	2019
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	420	2020
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	396	2022
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	365	2014
GMNN: Graph Markov Neural Networks M Qu, Y Bengio, J Tang ICML 2019, 2019	321	2019
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	263	2021
Adversarial network embedding Q Dai, Q Li, J Tang, D Wang Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018	254	2018
Pre-training Molecular Graph Representation with 3D Geometry S Liu, H Wang, W Liu, J Lasenby, H Guo, J Tang ICLR 2022, 2022	250	2022
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	246	2021
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	193	2017
Learning gradient fields for molecular conformation generation C Shi, S Luo, M Xu, J Tang ICML’21, 2021	190	2021

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