Kewei Tu, Assistant Professor

The publisher:袁益民Release time:2018-08-20浏览次数:106

Kewei Tu, Assistant Professor

Tel:  (021) 20685089
Email: tukw@@shanghaitech.edu.cn
Office: Room 1A-304B, SIST Building
Major: CS
Website: http://faculty.sist.shanghaitech.edu.cn/faculty/tukw/
Kewei Tu Research Group Recruitment (Click Here)

RESEARCH INTERESTS


  • Natural Language Processing

  • Machine Learning

  • Knowledge Representation

  • Computer Vision

  • ArtificialIntelligence


BIOGRAPHY

Dr. Kewei Tu is an Assistant Professor with the School of Information Science and Technology at ShanghaiTech University, China. He received BS and MS degrees in Computer Science and Technology from Shanghai Jiaotong University, China in 2002 and 2005 respectively and received a PhD degree in Computer Science from Iowa State University, USA in 2012. During 2012-2014, he worked as a postdoctoral researcher at the Center for Vision, Cognition, Learning and Autonomy, Departments of Statistics and Computer Science of the University of California, Los Angeles, USA. His research lies in the areas of natural language processing, machine learning, and artificial intelligence in general, with a focus on the representation, learning and application of stochastic grammars.

See his homepage for more information: http://faculty.sist.shanghaitech.edu.cn/faculty/tukw/

SELECTED PUBLICATIONS

1.Lin Qiu, Hao Zhou, Yanru Qu, Weinan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu,and Yong Yu, QA4IE: A Question Answering based Framework for Information Extraction. In the 17th International Semantic Web Conference (ISWC 2018), Monterey, California, USA, October 8–12, 2018.

2.Yanpeng Zhao, Liwen Zhang,and Kewei Tu, Gaussian Mixture Latent Vector Grammars. In the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, July 15–20, 2018.

3.Jun Mei, Yong Jiang,and Kewei Tu, Maximum A Posteriori Inference in Sum-Product Networks. In the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, Lousiana, USA, February 2–7, 2018.

4.Yong Jiang, Yang Zhou,and Kewei Tu, Learningand Evaluation of Latent Dependency Forest Models. To appear in Neural Computingand Applications.

5.1. Chen Zhu, Yanpeng Zhao, Shuaiyi Huang, Kewei Tu, and Yi Ma, Structured Attentions for Visual Question Answering. In the International Conference on Computer Vision (ICCV 2017), Venice, Italy, October 22-29, 2017.

6.Yong Jiang, Wenjuan Han, and Kewei Tu, Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 711, 2017.

7.Wenjuan Han, Yong Jiang, and Kewei Tu, Dependency Grammar Induction with Neural Lexicalization and Big Training Data. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 711, 2017.

8.Jiong Cai, Yong Jiang, and Kewei Tu, CRF Autoencoder for Unsupervised Dependency Parsing. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 711, 2017.

9.Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, and Dan Goldwasser, Semi-supervised Structured Prediction with Neural CRF Autoencoder. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 711, 2017.

10.Shanbo Chu, Yong Jiang and Kewei Tu, Latent Dependency Forest Models. In the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), San Francisco, California, USA, February 49, 2017.

11.Lin Qiu, Kewei Tu and Yong Yu, Context-Dependent Sense Embedding. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016.

12.Yong Jiang, Wenjuan Han and Kewei Tu, Unsupervised Neural Dependency Parsing. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016.

13.Kewei Tu, Modified Dirichlet Distribution: Allowing Negative Parameters to Induce Stronger Sparsity. In the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016. 

14.Kewei Tu, Stochastic And-Or Grammars: A Unified Framework and Logic Perspective. In the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York City, USA, July 9-15, 2016.