Zhe Gan

Publications

arXiv preprints

Yen-Chun Chen*, Linjie Li*, Licheng Yu*, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng and Jingjing Liu “UNITER: Learning UNiversal Image-TExt Representations”, 2019. PDF
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein and Jingjing Liu “FreeLB: Enhanced Adversarial Training for Language Understanding”, 2019. PDF
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang and Jingjing Liu “Hierarchical Graph Network for Multi-hop Question Answering”, 2019. PDF
Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu and Jingjing Liu “Distilling the Knowledge of BERT for Text Generation”, 2019. PDF
Jiacheng Xu, Zhe Gan, Yu Cheng and Jingjing Liu “Discourse-Aware Neural Extractive Model for Text Summarization”, 2019. PDF
Wenhu Chen, Zhe Gan, Linjie Li, Yu Cheng, William Wang and Jingjing Liu “Meta Module Network for Compositional Visual Reasoning”, 2019. PDF
Shuyang Dai, Yu Cheng, Yizhe Zhang, Zhe Gan, Jingjing Liu and Lawrence Carin “Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation”, 2019. PDF
Yu Cheng, Zhe Gan, Yitong Li, Jingjing Liu and Jianfeng Gao “Sequential Attention GAN for Interactive Image Editing via Dialogue”, 2019. PDF
Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Liqun Chen, Dinghan Shen, Guoyin Wang and Lawrence Carin “Sequence Generation with Guider Network”, 2018. PDF

2020

Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang and Lawrence Carin “Graph-Driven Generative Models for Heterogeneous Multi-Task Learning”, Proc. American Association of Artificial Intelligence (AAAI), 2020. PDF
Junjie Hu, Yu Cheng, Zhe Gan, Jingjing Liu, Jianfeng Gao and Graham Neubig “What Makes A Good Story? Designing Composite Rewards for Visual Storytelling”, Proc. American Association of Artificial Intelligence (AAAI), 2020. PDF

2019

Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao and Lawrence Carin “Improving Textual Network Learning with Variational Homophilic Embeddings”, Neural Information Processing Systems (NeurIPS), 2019. PDF
Siqi Sun, Yu Cheng, Zhe Gan and Jingjing Liu “Patient Knowledge Distillation for BERT Model Compression”, Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2019. PDF / Poster / Code
Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao and Hongning Wang “Adversarial Domain Adaptation for Machine Reading Comprehension”, Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2019. PDF / Poster
Dianqi Li, Yizhe Zhang, Zhe Gan, Yu Cheng, Chris Brockett, Ming-Ting Sun and Bill Dolan “Domain Adaptive Text Style Transfer”, Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2019. PDF / Code
Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner and Jianfeng Gao “TIGEr: Text-to-Image Grounding for Image Caption Evaluation”, Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2019. PDF / Poster
Linjie Li, Zhe Gan, Yu Cheng and Jingjing Liu “Relation-Aware Graph Attention Network for Visual Question Answering”, Int. Conf. on Computer Vision (ICCV), 2019. PDF / Supp / Poster / Code
Zhe Gan, Yu Cheng, Ahmed El Kholy, Linjie Li, Jingjing Liu and Jianfeng Gao “Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog”, Association for Computational Linguistics (ACL), 2019. PDF / Poster
Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, Jingjing Liu, Jianfeng Gao, Yejin Choi and Siddhartha Srinivasa “Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation”, Computer Vision and Pattern Recognition (CVPR), 2019. PDF / YouTube / Code (Oral)
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson and Jianfeng Gao “StoryGAN: A Sequential Conditional GAN for Story Visualization”, Computer Vision and Pattern Recognition (CVPR), 2019. PDF / Slides / Poster / Code
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen and Lawrence Carin “Topic-Guided Variational Autoencoders for Text Generation”, North American Chapter of the Association for Computational Linguistics (NAACL), 2019. PDF (Oral)
Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen and Lawrence Carin “Improving Sequence-to-Sequence Learning via Optimal Transport”, Int. Conf. Learning Representations (ICLR), 2019. PDF / Code
Qiuyuan Huang*, Zhe Gan*, Asli Celikyilmaz, Dapeng Wu, Jianfeng Wang and Xiaodong He “Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation”, Proc. American Association of Artificial Intelligence (AAAI), 2019. PDF (Spotlight)

2018

Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett and Bill Dolan “Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization”, Neural Information Processing Systems (NeurIPS), 2018. PDF / Blog / Code
Liqun Chen, Shuyang Dai, Chenyang Tao, Dinghan Shen, Zhe Gan, Haichao Zhang, Yizhe Zhang and Lawrence Carin “Adversarial Text Generation via Feature-Mover's Distance”, Neural Information Processing Systems (NeurIPS), 2018. PDF / Code
Xinyuan Zhang, Ricardo Henao, Zhe Gan, Yitong Li and Lawrence Carin “Multi-Label Learning from Medical Plain Text with Convolutional Residual Models”, Machine Learning for Healthcare (MLHC), 2018. PDF (Spotlight)
Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao and Lawrence Carin “JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets”, Int. Conf. Machine Learning (ICML), 2018. PDF / Supp / Poster / Slides / Code
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang and Xiaodong He “AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks”, Computer Vision and Pattern Recognition (CVPR), 2018. PDF / Poster / Code / Blog
Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh and Lawrence Carin “Topic Compositional Neural Language Model”, Artificial Intelligence and Statistics (AISTATS), 2018. PDF
Yunchen Pu, Martin Renqiang Min, Zhe Gan and Lawrence Carin “Adaptive Feature Abstraction for Translating Video to Text”, Proc. American Association of Artificial Intelligence (AAAI), 2018. PDF

2017

Zhe Gan*, Liqun Chen*, Weiyao Wang, Yunchen Pu, Yizhe Zhang, Hao Liu, Chunyuan Li and Lawrence Carin “Triangle Generative Adversarial Networks”, Neural Information Processing Systems (NeurIPS), 2017. PDF / Poster / Code
Yunchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li and Lawrence Carin “Adversarial Symmetric Variational Autoencoder”, Neural Information Processing Systems (NeurIPS), 2017. PDF
Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han and Lawrence Carin “VAE Learning via Stein Variational Gradient Descent”, Neural Information Processing Systems (NeurIPS), 2017. PDF
Yizhe Zhang, Dinghan Shen, Guoying Wang, Zhe Gan, Ricardo Henao and Lawrence Carin “Deconvolutional Paragraph Representation Learning”, Neural Information Processing Systems (NeurIPS), 2017. PDF / Code
Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He and Lawrence Carin “Learning Generic Sentence Representations Using Convolutional Neural Networks”, Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2017. PDF / Slides / Code (Oral)
Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen and Lawrence Carin “Adversarial Feature Matching for Text Generation”, Int. Conf. Machine Learning (ICML), 2017. PDF / Supp / Slides / Poster / Code
Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao and Lawrence Carin “Stochastic Gradient Monomial Gamma Sampler”, Int. Conf. Machine Learning (ICML), 2017. PDF / Supp / Slides / Poster / Code
Zhe Gan*, Chunyuan Li*, Changyou Chen, Yunchen Pu, Qinliang Su and Lawrence Carin “Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling”, Association for Computational Linguistics (ACL), 2017. PDF / Supp / Slides / Code (Oral)
Zhe Gan, Chuang Gan, Xiaodong He, Yunchen Pu, Kenneth Tran, Jianfeng Gao, Lawrence Carin and Li Deng “Semantic Compositional Networks for Visual Captioning”, Computer Vision and Pattern Recognition (CVPR), 2017. PDF / Slides / Slides2 / Poster / Poster2 / Video / Code (Spotlight)
Chuang Gan, Zhe Gan, Xiaodong He, Jianfeng Gao and Li Deng “StyleNet: Generating Attractive Visual Captions with Styles”, Computer Vision and Pattern Recognition (CVPR), 2017. PDF / Data
Zhe Gan, P.D. Singh, Ameet Joshi, Xiaodong He, Jianshu Chen, Jianfeng Gao and Li Deng “Character-level Deep Conflation for Business Data Analytics”, Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017. PDF / Slides / Code
Yin Xian, Yunchen Pu, Zhe Gan, Liang Lu and Andrew Thompson “Adaptive DCTNet for Audio Signal Classification”, Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017. PDF
Qinliang Su, Xuejun Liao, Chunyuan Li, Zhe Gan and Lawrence Carin “Unsupervised Learning with Truncated Gaussian Graphical Models”, Proc. American Association of Artificial Intelligence (AAAI), 2017. PDF (Oral)

2016

Yizhe Zhang, Zhe Gan and Lawrence Carin “Generating Text via Adversarial Training”, Workshop on Adversarial Training, NeurIPS 2016. PDF / Code
Yin Xian, Yunchen Pu, Zhe Gan, Liang Lu and Andrew Thompson “Modified DCTNet for Audio Signals Classification”, Journal of the Acoustical Society of America, 2016. Link
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens and Lawrence Carin “Variational Autoencoder for Deep Learning of Images, Labels and Captions”, Neural Information Processing Systems (NeurIPS), 2016. PDF / Poster
Jiaming Song, Zhe Gan and Lawrence Carin “Factored Temporal Sigmoid Belief Networks for Sequence Learning”, Int. Conf. Machine Learning (ICML), 2016. PDF / Supp / Poster / Slides
Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan and Lawrence Carin "Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification", Computer Vision and Pattern Recognition (CVPR), 2016. PDF / Supp / Poster / Slides (Spotlight)
Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li and Lawrence Carin “Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization”, Artificial Intelligence and Statistics (AISTATS), 2016. PDF / Poster / Slides / Code (Oral)

2015

Ricardo Henao, Zhe Gan, James Lu and Lawrence Carin "Deep Poisson Factor Modeling", Neural Information Processing Systems (NeurIPS), 2015. PDF / Supp / Poster / Code
Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson and Lawrence Carin "Deep Temporal Sigmoid Belief Networks for Sequence Modeling", Neural Information Processing Systems (NeurIPS), 2015. PDF / Poster / Slides / Code
Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson and Lawrence Carin "Scalable Deep Poisson Factor Analysis for Topic Modeling", Int. Conf. Machine Learning (ICML), 2015. PDF / Supp / Poster / Slides / Code
Zhe Gan, Ricardo Henao, David Carlson and Lawrence Carin "Learning Deep Sigmoid Belief Networks with Data Augmentation", Artificial Intelligence and Statistics (AISTATS), 2015. PDF / Supp / Poster / Code

Book Chapter

Zhe Gan, Xin Yuan, Ricardo Henao, Ephraim Tsalik and Lawrence Carin "Inference of Gene Networks Associated with the Host Response to Infectious Disease", Chapter 13 of the Book "Big Data Over Networks". Cambridge University Press. In Press. Link / PDF / Code

PhD Dissertation

Zhe Gan "Deep Generative Models for Vision and Language Intelligence", Duke University. PDF