Wenlin Wang (王文麟)

I'm a 4th-year Ph.D. Candidate in Department of Electrical and Computer Engineering at Duke University. My advisor is Prof. Lawrence Carin.

My primary research interests include Deep Learning, Statistical Machine Learning and Data Mining. Currently I am working on deep generative model and its applications in natural language processing and computer vision.

Prior to Duke University, I earned my Master's degree in Computer Science from Washington University in St. Louis in 2015 and Bachelor's degree in Electrical Engineering and Information Science from University of Science and Technology of China in 2013. In the summer of 2012, I was an exchanging student at University of Western Australia

Email: wenlin dot wang AT duke dot edu

Publication

• Improving Textual Network Embedding with Global Attention via Optimal Transport
L. Chen, G. Wang, C. Tao, D. Shen, P. Cheng, X. Zhang, Wenlin Wang, Y. Zhang and L. Carin
Annual Meeting of the Association for Computational Linguistics (ACL), 2019

• Topic-Guided Variational Auto-Encoder for Text Generation [PDF] (Oral)
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen and Lawrence Carin
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019

• A Convergence Analysis For a Class of Practical Variance-reduction Stochastic Gradient MCMC [PDF]
Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin
Science China Information Science, vol.62 , 2019

• InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks [PDF]
Qi Wei, Kai Fan, Wenlin Wang, Tianhang Zheng, Chakraborty Amit, Katherine Heller, Changyou Chen, Kui Ren
IEEE International Conference on Multimedia and Expo (ICME), 2019

• Distilled Wasserstein Learning for Word Embedding and Topic Modeling [PDF]
Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin
Neural Information Processing Systems (NeurIPS), 2018

• Topic Compositional Neural Language Model [PDF][Poster]
Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin
Artificial Intelligence and Statistics (AISTATS), 2018

• Continuous-Time Flows for Deep Generative Models [PDF]
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin
International Conference on Machine Learning (ICML), 2018

• Joint Embedding of Words and Labels for Text Classification [PDF]
Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, R. Henao, Lawrence Carin
Annual Meeting of the Association for Computational Linguistics (ACL), 2018 [Code]

• Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms [PDF]
Dinghan Shen, Guoyin Wang, Wenlin Wang, M.R. Min, Qinliang Su, Yizhe Zhang, Ricardo Henao, Lawrence Carin
Annual Meeting of the Association for Computational Linguistics (ACL) , 2018 [Code]

• NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing [PDF] (Oral)
Dinghan Shen, Qinliang Su, P. Chapfuwa, Wenlin Wang, Guoyin Wang, Ricardo Henao, Lawrence Carin
Annual Meeting of the Association for Computational Linguistics (ACL), 2018
Best Long Paper Award, Honorable Mention: 6/1018, 0.6% [Link]

• Zero-Shot Learning via Class-Conditioned Deep Generative Models [PDF][Slides] (Oral)
Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin
Proc. AAAI Conference on Artificial Intelligence (AAAI), 2018

• A Unified Particle-Optimization Framework for Scalable Bayesian Sampling [PDF](Oral)
Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen, Lawrence Carin
The Conference on Uncertainty in Artificial Intelligence (UAI), 2018

• Wide Compression: Tensor Ring Nets [PDF]
Wenqi Wang, Yifan Sun, Brian Eriksson, Wenlin Wang, Vaneet Aggarwal
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

• Deep Metric Learning with Data Summarization [PDF]
Wenlin Wang, Changyou Chen, Wenlin Chen, Piyush Rai, Lawrence Carin
European Conference on Machine Learning (ECML), 2016 [Code]

Workshops & Preprints

• Sequence Generation with Guider Network [PDF]
Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Liqun Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin
Workshop on Uncertainty & Robustness in Deep Learning, Internatial Conference on Machine Learning (ICML), 2019

• On Norm-Agnostic Robustness of Adversarial Training [PDF]
Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
Workshop on Real-world Sequential Decision Making, Internatial Conference on Machine Learning (ICML), 2019

• Earliness-Aware Deep Convolutional Networks for Early Time Series Classification [PDF]
Wenlin Wang, Changyou Chen, Wenqi Wang, Piyush Rai, Lawrence Carin

Experience

• Research Intern, Baidu Silicon Valley AI Lab (SVAIL), Sunnyvale, USA.
(06/2017-08/2017)

(05/2015-08/2015)
• Algorithm Engineer Intern, Institute of Data Science and Technologies (iDST), Alibaba, Beijing.