Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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 — Midea Group Co Ltd, Foshan, China (and 3 more)
- Jian Tang 0009 — Central University of Finance and Economics, School of Information, Beijing, China
- Jian Tang 0010 — Technical University of Madrid, Spain
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j7]Cheng Yang, Hao Wang, Jian Tang, Chuan Shi, Maosong Sun, Ganqu Cui, Zhiyuan Liu:
Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks. IEEE Trans. Neural Networks Learn. Syst. 34(5): 2271-2283 (2023)- [c73]Yu Li, Meng Qu, Jian Tang, Yi Chang:
Signed Laplacian Graph Neural Networks. AAAI 2023: 4444-4452 - [c72]Shengchao Liu, David Vázquez, Jian Tang, Pierre-André Noël:
Flaky Performances When Pretraining on Relational Databases (Student Abstract). AAAI 2023: 16266-16267 - [c71]Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang:
Learning on Large-scale Text-attributed Graphs via Variational Inference. ICLR 2023 - [c70]Shengchao Liu, Hongyu Guo, Jian Tang:
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching. ICLR 2023 - [c69]Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang:
Protein Sequence and Structure Co-Design with Equivariant Translation. ICLR 2023 - [c68]Yangtian Zhang, Huiyu Cai, Chence Shi, Jian Tang:
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking. ICLR 2023 - [c67]Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. ICLR 2023 - [c66]Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo, Jian Tang:
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining. ICML 2023: 21497-21526 - [c65]Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang:
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. ICML 2023: 38749-38767 - [i90]Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang:
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. CoRR abs/2301.12040 (2023) - [i89]Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang:
Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction. CoRR abs/2301.12068 (2023) - [i88]Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar:
A Text-guided Protein Design Framework. CoRR abs/2302.04611 (2023) - [i87]Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Enhancing Protein Language Models with Structure-based Encoder and Pre-training. CoRR abs/2303.06275 (2023) - [i86]Fang Sun, Zhihao Zhan, Hongyu Guo, Ming Zhang, Jian Tang:
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow. CoRR abs/2304.12825 (2023) - [i85]Shengchao Liu, Weitao Du, Zhiming Ma, Hongyu Guo, Jian Tang:
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining. CoRR abs/2305.18407 (2023) - [i84]Yangtian Zhang, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang:
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing. CoRR abs/2306.01794 (2023) - [i83]Jiarui Lu, Bozitao Zhong, Jian Tang:
Score-based Enhanced Sampling for Protein Molecular Dynamics. CoRR abs/2306.03117 (2023) - [i82]Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer T. Chayes, Hongyu Guo, Jian Tang:
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. CoRR abs/2306.09375 (2023) - [i81]Andreea Deac, Jian Tang:
Evolving Computation Graphs. CoRR abs/2306.12943 (2023) - 2022
- [c64]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 - [c63]Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang:
Structured Multi-task Learning for Molecular Property Prediction. AISTATS 2022: 8906-8920 - [c62]Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, Christian S. Jensen:
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning. ICDE 2022: 2873-2885 - [c61]Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang:
Pre-training Molecular Graph Representation with 3D Geometry. ICLR 2022 - [c60]Meng Qu, Huiyu Cai, Jian Tang:
Neural Structured Prediction for Inductive Node Classification. ICLR 2022 - [c59]Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. ICLR 2022 - [c58]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. ICML 2022: 23213-23236 - [c57]Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang:
Neural-Symbolic Models for Logical Queries on Knowledge Graphs. ICML 2022: 27454-27478 - [c56]Michael Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang:
Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022 - [c55]Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang:
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding. NeurIPS 2022 - [i80]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) - [i79]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) - [i78]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) - [i77]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) - [i76]Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang:
Structured Multi-task Learning for Molecular Property Prediction. CoRR abs/2203.04695 (2022) - [i75]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) - [i74]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) - [i73]Meng Qu, Huiyu Cai, Jian Tang:
Neural Structured Prediction for Inductive Node Classification. CoRR abs/2204.07524 (2022) - [i72]Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang:
Neural-Symbolic Models for Logical Queries on Knowledge Graphs. CoRR abs/2205.10128 (2022) - [i71]Minghao Xu, Yuanfan Guo, Xuanyu Zhu, Jiawen Li, Zhenbang Sun, Jian Tang, Yi Xu, Bingbing Ni:
HIRL: A General Framework for Hierarchical Image Representation Learning. CoRR abs/2205.13159 (2022) - [i70]Dingmin Wang, Shengchao Liu, Hanchen Wang, Linfeng Song, Jian Tang, Song Le, Bernardo Cuenca Grau, Qi Liu:
Augmenting Message Passing by Retrieving Similar Graphs. CoRR abs/2206.00362 (2022) - [i69]Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang:
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding. CoRR abs/2206.02096 (2022) - [i68]Zhaocheng Zhu, Xinyu Yuan, Louis-Pascal A. C. Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang:
Learning to Efficiently Propagate for Reasoning on Knowledge Graphs. CoRR abs/2206.04798 (2022) - [i67]Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt J. Kusner, Joan Lasenby, Qi Liu:
Evaluating Self-Supervised Learning for Molecular Graph Embeddings. CoRR abs/2206.08005 (2022) - [i66]Shengchao Liu, Hongyu Guo, Jian Tang:
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching. CoRR abs/2206.13602 (2022) - [i65]Songtao Liu, Rex Ying, Zuobai Zhang, Peilin Zhao, Jian Tang, Lu Lin, Dinghao Wu:
Metro: Memory-Enhanced Transformer for Retrosynthetic Planning via Reaction Tree. CoRR abs/2209.15315 (2022) - [i64]Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong, Jian Tang:
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking. CoRR abs/2210.06069 (2022) - [i63]Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang:
Inductive Logical Query Answering in Knowledge Graphs. CoRR abs/2210.08008 (2022) - [i62]Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang:
Protein Sequence and Structure Co-Design with Equivariant Translation. CoRR abs/2210.08761 (2022) - [i61]Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang:
Learning on Large-scale Text-attributed Graphs via Variational Inference. CoRR abs/2210.14709 (2022) - [i60]Shengchao Liu, David Vázquez, Jian Tang, Pierre-André Noël:
Flaky Performances when Pretraining on Relational Databases. CoRR abs/2211.05213 (2022) - [i59]Minghao Xu, Yuanfan Guo, Yi Xu, Jian Tang, Xinlei Chen, Yuandong Tian:
EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data. CoRR abs/2211.12941 (2022) - [i58]Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar:
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing. CoRR abs/2212.10789 (2022) - 2021
- [j6]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) - [j5]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) - [c54]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 - [c53]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 - [c52]Meng Qu, Junkun Chen, Louis-Pascal A. C. Xhonneux, Yoshua Bengio, Jian Tang:
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs. ICLR 2021 - [c51]Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang:
Learning Neural Generative Dynamics for Molecular Conformation Generation. ICLR 2021 - [c50]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 - [c49]Chence Shi, Shitong Luo, Minkai Xu, Jian Tang:
Learning Gradient Fields for Molecular Conformation Generation. ICML 2021: 9558-9568 - [c48]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 - [c47]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 - [c46]Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang:
Unsupervised Path Representation Learning with Curriculum Negative Sampling. IJCAI 2021: 3286-3292 - [c45]Jian Tang, Fei Wang, Feixiong Cheng:
Artificial Intelligence for Drug Discovery. KDD 2021: 4074-4075 - [c44]Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang:
Joint Modeling of Visual Objects and Relations for Scene Graph Generation. NeurIPS 2021: 7689-7702 - [c43]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. NeurIPS 2021: 15529-15542 - [c42]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 - [c41]Shitong Luo, Chence Shi, Minkai Xu, Jian Tang:
Predicting Molecular Conformation via Dynamic Graph Score Matching. NeurIPS 2021: 19784-19795 - [c40]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 - [i57]Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang:
Learning Neural Generative Dynamics for Molecular Conformation Generation. CoRR abs/2102.10240 (2021) - [i56]Chence Shi, Shitong Luo, Minkai Xu, Jian Tang:
Learning Gradient Fields for Molecular Conformation Generation. CoRR abs/2105.03902 (2021) - [i55]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) - [i54]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) - [i53]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) - [i52]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) - [i51]Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, Bin Yang:
Unsupervised Path Representation Learning with Curriculum Negative Sampling. CoRR abs/2106.09373 (2021) - [i50]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. CoRR abs/2110.05442 (2021) - [i49]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) - [i48]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
- [c39]Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega:
Deep Geometric Knowledge Distillation with Graphs. ICASSP 2020: 8484-8488 - [c38]Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang:
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. ICLR 2020 - [c37]Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang:
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. ICLR 2020 - [c36]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 - [c35]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 - [c34]Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang:
A Graph to Graphs Framework for Retrosynthesis Prediction. ICML 2020: 8818-8827 - [c33]Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang:
Continuous Graph Neural Networks. ICML 2020: 10432-10441 - [c32]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 - [i47]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) - [i46]Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang:
A Graph to Graphs Framework for Retrosynthesis Prediction. CoRR abs/2003.12725 (2020) - [i45]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) - [i44]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) - [i43]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) - [i42]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) - [i41]Andreea Deac, Pierre-Luc Bacon, Jian Tang:
Graph neural induction of value iteration. CoRR abs/2009.12604 (2020) - [i40]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) - [i39]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) - [i38]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
XLVIN: eXecuted Latent Value Iteration Nets. CoRR abs/2010.13146 (2020) - [i37]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) - [i36]Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, Yong Yu:
Sobolev Wasserstein GAN. CoRR abs/2012.03420 (2020) - [i35]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:
Utilising Graph Machine Learning within Drug Discovery and Development. CoRR abs/2012.05716 (2020)
2010 – 2019
- 2019
- [j4]Shagun Sodhani, Meng Qu, Jian Tang:
Attending Over Triads for Learning Signed Network Embedding. Frontiers Big Data 2: 6 (2019) - [c31]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 - [c30]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 - [c29]Huawei Shen, Jian Tang, Peng Bao:
GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications. CIKM 2019: 2997-2998 - [c28]Carlos Eduardo Rosar Kós Lassance, Vincent Gripon, Jian Tang, Antonio Ortega:
Structural Robustness for Deep Learning Architectures. DSW 2019: 125-129 - [c27]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 - [c26]Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang:
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. ICLR (Poster) 2019 - [c25]Meng Qu, Yoshua Bengio, Jian Tang:
GMNN: Graph Markov Neural Networks. ICML 2019: 5241-5250 - [c24]Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, Zhiyuan Liu:
Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks. IJCAI 2019: 4033-4039 - [c23]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 - [c22]Meng Qu, Jian Tang:
Probabilistic Logic Neural Networks for Reasoning. NeurIPS 2019: 7710-7720 - [c21]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 - [c20]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 - [c19]Zhaocheng Zhu, Shizhen Xu, Jian Tang, Meng Qu:
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding. WWW 2019: 2494-2504 - [i34]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) - [i33]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) - [i32]Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang:
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding. CoRR abs/1903.00757 (2019) - [i31]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) - [i30]