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ORCIDJian Tang 0005
唐建
Person information
- unicode name: 唐建
- affiliation (since 2017): HEC Montreal, QC, Canada
- affiliation (since 2017): Mila - Quebec AI Institute, Montreal, QC, Canada
- affiliation (2016 - 2017): University of Michigan, Ann Arbor, MI, USA
- affiliation (2014 - 2016): Microsoft Research Asia, Beijing, China
- affiliation (PhD 2014): Peking University, School of Electronics Engineering and Computer Science, Beijing, China
Other persons with the same name
- Jian Tang — disambiguation page
- Jian Tang 0001 — Memorial University of Newfoundland, Department of Computer Science, St. John's, Canada
- Jian Tang 0002
![[0000-0003-2458-6475]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-2458-6475")
— Lanzhou University, School of Mathematics and Statistics, China (and 2 more) - Jian Tang 0003 — Beijing University of Technology, College of Electronic and Control Engineering, China (and 1 more)
- Jian Tang 0004 — Wuhan University, GNSS Research Center, China (and 1 more)
- Jian Tang 0006 — Quzhou University, College of Mechanical Engineering, China
- Jian Tang 0007 — Sun Yat-sen University, School of Electronics and Information Engineering, Guangzhou, China
- Jian Tang 0008 — Syracuse University, Department of Electrical Engineering and Computer Science, NY, USA (and 2 more)
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2020 – today
- 2022
[c53]Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, Hong Chen:
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering. ACL (1) 2022: 5773-5784- [c52]Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang:
Structured Multi-task Learning for Molecular Property Prediction. AISTATS 2022: 8906-8920 - [i61]Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli:
Generative Coarse-Graining of Molecular Conformations. CoRR abs/2201.12176 (2022) - [i60]Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang:
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery. CoRR abs/2202.08320 (2022) - [i59]Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, Hong Chen:
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering. CoRR abs/2202.13296 (2022) - [i58]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation. CoRR abs/2203.02923 (2022) - [i57]Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang:
Structured Multi-task Learning for Molecular Property Prediction. CoRR abs/2203.04695 (2022) - [i56]Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. CoRR abs/2203.06125 (2022) - [i55]Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, Christian S. Jensen:
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning - Extended Version. CoRR abs/2203.16110 (2022) - [i54]Meng Qu, Huiyu Cai, Jian Tang:
Neural Structured Prediction for Inductive Node Classification. CoRR abs/2204.07524 (2022) - 2021
- [j5]Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King:
Utilizing graph machine learning within drug discovery and development. Briefings Bioinform. 22(6) (2021) - [j4]Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang:
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation. Trans. Assoc. Comput. Linguistics 9: 176-194 (2021) - [c51]Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang:
GraphMix: Improved Training of GNNs for Semi-Supervised Learning. AAAI 2021: 10024-10032 - [c50]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau-Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Christopher J. Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. ICLR 2021 - [c49]Meng Qu, Junkun Chen, Louis-Pascal A. C. Xhonneux, Yoshua Bengio, Jian Tang:
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs. ICLR 2021 - [c48]Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang:
Learning Neural Generative Dynamics for Molecular Conformation Generation. ICLR 2021 - [c47]Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, Hongyu Guo:
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. ICML 2021: 904-913 - [c46]Chence Shi, Shitong Luo, Minkai Xu, Jian Tang:
Learning Gradient Fields for Molecular Conformation Generation. ICML 2021: 9558-9568 - [c45]Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez-Bombarelli, Jian Tang:
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. ICML 2021: 11537-11547 - [c44]Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang:
Self-supervised Graph-level Representation Learning with Local and Global Structure. ICML 2021: 11548-11558 - [c43]Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang:
Unsupervised Path Representation Learning with Curriculum Negative Sampling. IJCAI 2021: 3286-3292 - [c42]Jian Tang, Fei Wang, Feixiong Cheng:
Artificial Intelligence for Drug Discovery. KDD 2021: 4074-4075 - [c41]Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang:
Joint Modeling of Visual Objects and Relations for Scene Graph Generation. NeurIPS 2021: 7689-7702 - [c40]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. NeurIPS 2021: 15529-15542 - [c39]Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang:
How to transfer algorithmic reasoning knowledge to learn new algorithms? NeurIPS 2021: 19500-19512 - [c38]Shitong Luo, Chence Shi, Minkai Xu, Jian Tang:
Predicting Molecular Conformation via Dynamic Graph Score Matching. NeurIPS 2021: 19784-19795 - [c37]Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, Jian Tang:
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. NeurIPS 2021: 29476-29490 - [i53]Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang:
Learning Neural Generative Dynamics for Molecular Conformation Generation. CoRR abs/2102.10240 (2021) - [i52]Chence Shi, Shitong Luo, Minkai Xu, Jian Tang:
Learning Gradient Fields for Molecular Conformation Generation. CoRR abs/2105.03902 (2021) - [i51]Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez-Bombarelli, Jian Tang:
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. CoRR abs/2105.07246 (2021) - [i50]Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang:
Self-supervised Graph-level Representation Learning with Local and Global Structure. CoRR abs/2106.04113 (2021) - [i49]Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, Jian Tang:
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. CoRR abs/2106.06935 (2021) - [i48]Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, Hongyu Guo:
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. CoRR abs/2106.07801 (2021) - [i47]Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang:
Unsupervised Path Representation Learning with Curriculum Negative Sampling. CoRR abs/2106.09373 (2021) - [i46]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. CoRR abs/2110.05442 (2021) - [i45]Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang:
Pre-training Molecular Graph Representation with 3D Geometry. CoRR abs/2110.07728 (2021) - [i44]Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang:
How to transfer algorithmic reasoning knowledge to learn new algorithms? CoRR abs/2110.14056 (2021) - 2020
- [c36]Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega:
Deep Geometric Knowledge Distillation with Graphs. ICASSP 2020: 8484-8488 - [c35]Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang:
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. ICLR 2020 - [c34]Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang:
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. ICLR 2020 - [c33]Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam M. J. Thomas, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio:
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. ICML 2020: 3668-3679 - [c32]Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux, Jian Tang:
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs. ICML 2020: 7867-7876 - [c31]Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang:
A Graph to Graphs Framework for Retrosynthesis Prediction. ICML 2020: 8818-8827 - [c30]Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang:
Continuous Graph Neural Networks. ICML 2020: 10432-10441 - [c29]Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang:
Graph Policy Network for Transferable Active Learning on Graphs. NeurIPS 2020 - [i43]Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang:
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. CoRR abs/2001.09382 (2020) - [i42]Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang:
A Graph to Graphs Framework for Retrosynthesis Prediction. CoRR abs/2003.12725 (2020) - [i41]Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio:
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. CoRR abs/2004.12485 (2020) - [i40]Hannah Alsdurf, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud'homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Franois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, Yun William Yu:
COVI White Paper. CoRR abs/2005.08502 (2020) - [i39]Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang:
Graph Policy Network for Transferable Active Learning on Graphs. CoRR abs/2006.13463 (2020) - [i38]Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux, Jian Tang:
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs. CoRR abs/2007.02387 (2020) - [i37]Andreea Deac, Pierre-Luc Bacon, Jian Tang:
Graph neural induction of value iteration. CoRR abs/2009.12604 (2020) - [i36]Meng Qu, Junkun Chen, Louis-Pascal A. C. Xhonneux, Yoshua Bengio, Jian Tang:
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs. CoRR abs/2010.04029 (2020) - [i35]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B. Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. CoRR abs/2010.12536 (2020) - [i34]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
XLVIN: eXecuted Latent Value Iteration Nets. CoRR abs/2010.13146 (2020) - [i33]Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St-Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David L. Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Joseph Pal, Joanna Merckx, Eilif B. Müller, Yoshua Bengio:
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing. CoRR abs/2010.16004 (2020) - [i32]Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, Yong Yu:
Sobolev Wasserstein GAN. CoRR abs/2012.03420 (2020) - [i31]Hangrui Bi, Hengyi Wang, Chence Shi, Jian Tang:
Non-autoregressive electron flow generation for reaction prediction. CoRR abs/2012.12124 (2020)
2010 – 2019
- 2019
- [j3]Shagun Sodhani, Meng Qu, Jian Tang:
Attending Over Triads for Learning Signed Network Embedding. Frontiers Big Data 2: 6 (2019) - [c28]Luchen Liu, Haoran Li, Zhiting Hu, Haoran Shi, Zichang Wang, Jian Tang, Ming Zhang:
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction. AMIA 2019 - [c27]Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang:
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. CIKM 2019: 1161-1170 - [c26]Huawei Shen, Jian Tang, Peng Bao:
GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications. CIKM 2019: 2997-2998 - [c25]Carlos Eduardo Rosar Kós Lassance, Vincent Gripon, Jian Tang, Antonio Ortega:
Structural Robustness for Deep Learning Architectures. DSW 2019: 125-129 - [c24]Myriam Bontonou, Carlos Eduardo Rosar Kós Lassance, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega:
Introducing Graph Smoothness Loss for Training Deep Learning Architectures. DSW 2019: 160-164 - [c23]Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang:
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. ICLR (Poster) 2019 - [c22]Meng Qu, Yoshua Bengio, Jian Tang:
GMNN: Graph Markov Neural Networks. ICML 2019: 5241-5250 - [c21]Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, Zhiyuan Liu:
Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks. IJCAI 2019: 4033-4039 - [c20]Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang:
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. NeurIPS 2019: 512-522 - [c19]Meng Qu, Jian Tang:
Probabilistic Logic Neural Networks for Reasoning. NeurIPS 2019: 7710-7720 - [c18]Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng, Jian-Yun Nie:
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases. SIGIR 2019: 755-764 - [c17]Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang:
Session-Based Social Recommendation via Dynamic Graph Attention Networks. WSDM 2019: 555-563 - [c16]Zhaocheng Zhu, Shizhen Xu, Jian Tang, Meng Qu:
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding. WWW 2019: 2494-2504 - [i30]Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang:
Session-based Social Recommendation via Dynamic Graph Attention Networks. CoRR abs/1902.09362 (2019) - [i29]Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang:
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. CoRR abs/1902.10197 (2019) - [i28]Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang:
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding. CoRR abs/1903.00757 (2019) - [i27]Luchen Liu, Haoran Li, Zhiting Hu, Haoran Shi, Zichang Wang, Jian Tang, Ming Zhang:
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction. CoRR abs/1903.08652 (2019) - [i26]Myriam Bontonou, Carlos Eduardo Rosar Kós Lassance, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega:
Introducing Graph Smoothness Loss for Training Deep Learning Architectures. CoRR abs/1905.00301 (2019) - [i25]Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Liò, Jian Tang:
Drug-Drug Adverse Effect Prediction with Graph Co-Attention. CoRR abs/1905.00534 (2019) - [i24]Meng Qu, Yoshua Bengio, Jian Tang:
GMNN: Graph Markov Neural Networks. CoRR abs/1905.06214 (2019) - [i23]Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng, Jian-Yun Nie:
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases. CoRR abs/1905.07689 (2019) - [i22]Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang:
Learning Powerful Policies by Using Consistent Dynamics Model. CoRR abs/1906.04355 (2019) - [i21]Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang:
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. CoRR abs/1906.07159 (2019) - [i20]Meng Qu, Jian Tang:
Probabilistic Logic Neural Networks for Reasoning. CoRR abs/1906.08495 (2019) - [i19]Weiping Song, Zhijian Duan, Ziqing Yang, Hao Zhu, Ming Zhang, Jian Tang:
Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning. CoRR abs/1906.09506 (2019) - [i18]Meng Qu, Jian Tang, Yoshua Bengio:
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning. CoRR abs/1907.03179 (2019) - [i17]Fan-Yun Sun, Jordan Hoffmann, Jian Tang:
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. CoRR abs/1908.01000 (2019) - [i16]Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel D. Sellier, Yoshua Bengio:
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures. CoRR abs/1909.00949 (2019) - [i15]Carlos Eduardo Rosar Kós Lassance, Vincent Gripon, Jian Tang, Antonio Ortega:
Structural Robustness for Deep Learning Architectures. CoRR abs/1909.05095 (2019) - [i14]Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang:
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. CoRR abs/1909.11715 (2019) - [i13]Carlos Eduardo Rosar Kós Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega:
Deep geometric knowledge distillation with graphs. CoRR abs/1911.03080 (2019) - [i12]Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhiyuan Liu, Juanzi Li, Jian Tang:
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation. CoRR abs/1911.06136 (2019) - [i11]Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang:
Continuous Graph Neural Networks. CoRR abs/1912.00967 (2019) - 2018
- [j2]Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park, Jaegul Choo:
PixelSNE: Pixel-Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen-Resolution Precision. Comput. Graph. Forum 37(3): 267-276 (2018) - [c15]Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang:
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction. AAAI 2018: 109-116 - [c14]Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang:
DeepInf: Social Influence Prediction with Deep Learning. KDD 2018: 2110-2119 - [c13]Meng Qu, Jian Tang, Jiawei Han:
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning. WSDM 2018: 468-476 - [i10]Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang:
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction. CoRR abs/1803.04837 (2018) - [i9]Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang:
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. CoRR abs/1810.11921 (2018) - 2017
- [c12]Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han:
An Attention-based Collaboration Framework for Multi-View Network Representation Learning. CIKM 2017: 1767-1776 - [i8]Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han:
An Attention-based Collaboration Framework for Multi-View Network Representation Learning. CoRR abs/1709.06636 (2017) - 2016
- [c11]Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei:
Visualizing Large-scale and High-dimensional Data. WWW 2016: 287-297 - [i7]Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei:
Visualization Large-scale and High-dimensional Data. CoRR abs/1602.00370 (2016) - [i6]Jian Tang, Meng Qu, Qiaozhu Mei:
Identity-sensitive Word Embedding through Heterogeneous Networks. CoRR abs/1611.09878 (2016) - [i5]Jian Tang, Yifan Yang, Samuel Carton, Ming Zhang, Qiaozhu Mei:
Context-aware Natural Language Generation with Recurrent Neural Networks. CoRR abs/1611.09900 (2016) - [i4]Jian Tang, Ming Zhang, Qiaozhu Mei:
Less is More: Learning Prominent and Diverse Topics for Data Summarization. CoRR abs/1611.09921 (2016) - 2015
- [c10]Jian Tang, Meng Qu, Qiaozhu Mei:
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. KDD 2015: 1165-1174 - [c9]Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei:
LINE: Large-scale Information Network Embedding. WWW 2015: 1067-1077 - [i3]Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei:
LINE: Large-scale Information Network Embedding. CoRR abs/1503.03578 (2015) - [i2]Jian Tang, Meng Qu, Qiaozhu Mei:
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. CoRR abs/1508.00200 (2015) - 2014
- [c8]Yong Luo, Jian Tang, Jun Yan, Chao Xu, Zheng Chen:
Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network. AAAI 2014: 1982-1988 - [c7]Jian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei, Ming Zhang:
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis. ICML 2014: 190-198 - [i1]Jian Tang, Ming Zhang, Qiaozhu Mei:
"Look Ma, No Hands!" A Parameter-Free Topic Model. CoRR abs/1409.2993 (2014) - 2013
- [c6]Jian Tang, Ming Zhang, Qiaozhu Mei:
One theme in all views: modeling consensus topics in multiple contexts. KDD 2013: 5-13 - 2012
- [c5]Lei Zhang, Jian Tang, Ming Zhang:
Integrating Temporal Usage Pattern into Personalized Tag Prediction. APWeb 2012: 354-365 - 2011
- [j1]Bolanle Adefowoke Ojokoh, Ming Zhang, Jian Tang:
A trigram hidden Markov model for metadata extraction from heterogeneous references. Inf. Sci. 181(9): 1538-1551 (2011) - [c4]Jian Tang, Jun Yan, Lei Ji, Ming Zhang, Shaodan Guo, Ning Liu, Xianfang Wang, Zheng Chen:
Collaborative Users' Brand Preference Mining across Multiple Domains from Implicit Feedbacks. AAAI 2011 - [c3]Jian Tang, Ning Liu, Jun Yan, Yelong Shen, Shaodan Guo, Bin Gao, Shuicheng Yan, Ming Zhang:
Learning to rank audience for behavioral targeting in display ads. CIKM 2011: 605-610 - [c2]Ming Zhang, Sheng Feng, Jian Tang, Bolanle Adefowoke Ojokoh, Guojun Liu:
Co-Ranking Multiple Entities in a Heterogeneous Network: Integrating Temporal Factor and Users' Bookmarks. ICADL 2011: 202-211 - 2010
- [c1]Fei Yan, Ming Zhang, Jian Tang, Tao Sun, Zhi-Hong Deng, Long Xiao:
Users' Book-Loan Behaviors Analysis and Knowledge Dependency Mining. WAIM 2010: 206-217
Coauthor Index