Jian Tang (唐建)
Jian Tang (唐建)
Assistant Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI Chair
Bestätigte E-Mail-Adresse bei hec.ca - Startseite
Zitiert von
Zitiert von
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
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
Rotate: Knowledge graph embedding by relational rotation in complex space
Z Sun, ZH Deng, JY Nie, J Tang
ICLR 2019, 2019
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
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
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
Adversarial network embedding
Q Dai, Q Li, J Tang, D Wang
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
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
GMNN: Graph Markov Neural Networks
M Qu, Y Bengio, J Tang
ICML 2019, 2019
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
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
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
Context-aware natural language generation with recurrent neural networks
J Tang, Y Yang, S Carton, M Zhang, Q Mei
arXiv preprint arXiv:1611.09900, 2016
One theme in all views: modeling consensus topics in multiple contexts
J Tang, M Zhang, Q Mei
Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013
Artificial intelligence in COVID-19 drug repurposing
Y Zhou, F Wang, J Tang, R Nussinov, F Cheng
The Lancet Digital Health, 2020
Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices
X Ma, FM Guo, W Niu, X Lin, J Tang, K Ma, B Ren, Y Wang
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5117-5124, 2020
AutoCompress: An automatic DNN structured pruning framework for ultra-high compression rates
N Liu, X Ma, Z Xu, Y Wang, J Tang, J Ye
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4876-4883, 2020
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Z Zhu, S Xu, M Qu, J Tang
The World Wide Web Conference, 2494-2504, 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
Data poisoning attack against unsupervised node embedding methods
M Sun, J Tang, H Li, B Li, C Xiao, Y Chen, D Song
arXiv preprint arXiv:1810.12881, 2018
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