Zhe Gan

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Duke University ∙ Microsoft

Research Assistant at Duke University

VIOLIN: A Large-Scale Dataset for Video-and-Language Inference

We introduce a new task, Video-and-Language Inference, for joint multimo...
03/25/2020 ∙ by Jingzhou Liu, et al. ∙ 17 ∙ share

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BachGAN: High-Resolution Image Synthesis from Salient Object Layout

We propose a new task towards more practical application for image gener...
03/26/2020 ∙ by Yandong Li, et al. ∙ 7 ∙ share

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Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation

We present FAST NAVIGATOR, a general framework for action decoding, whic...
03/06/2019 ∙ by Liyiming Ke, et al. ∙ 6 ∙ share

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POINTER: Constrained Text Generation via Insertion-based Generative Pre-training

Large-scale pre-trained language models, such as BERT and GPT-2, have ac...
05/01/2020 ∙ by Yizhe Zhang, et al. ∙ 5 ∙ share

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HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

We present HERO, a Hierarchical EncodeR for Omni-representation learning...
05/01/2020 ∙ by Linjie Li, et al. ∙ 3 ∙ share

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AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

In this paper, we propose an Attentional Generative Adversarial Network ...
11/28/2017 ∙ by Tao Xu, et al. ∙ 0 ∙ share

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Triangle Generative Adversarial Networks

A Triangle Generative Adversarial Network (Δ-GAN) is developed for semi-...
09/19/2017 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Stochastic Gradient Monomial Gamma Sampler

Recent advances in stochastic gradient techniques have made it possible ...
06/05/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Unsupervised Learning with Truncated Gaussian Graphical Models

Gaussian graphical models (GGMs) are widely used for statistical modelin...
11/15/2016 ∙ by Qinliang Su, et al. ∙ 0 ∙ share

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Variational Autoencoder for Deep Learning of Images, Labels and Captions

A novel variational autoencoder is developed to model images, as well as...
09/28/2016 ∙ by Yunchen Pu, et al. ∙ 0 ∙ share

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Factored Temporal Sigmoid Belief Networks for Sequence Learning

Deep conditional generative models are developed to simultaneously learn...
05/22/2016 ∙ by Jiaming Song, et al. ∙ 0 ∙ share

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Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) methods are Bayes...
12/25/2015 ∙ by Changyou Chen, et al. ∙ 0 ∙ share

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Deep Temporal Sigmoid Belief Networks for Sequence Modeling

Deep dynamic generative models are developed to learn sequential depende...
09/23/2015 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Deconvolutional Paragraph Representation Learning

Learning latent representations from long text sequences is an important...
08/16/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Adversarial Feature Matching for Text Generation

The Generative Adversarial Network (GAN) has achieved great success in g...
06/12/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Character-level Deep Conflation for Business Data Analytics

Connecting different text attributes associated with the same entity (co...
02/08/2017 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling

Recurrent neural networks (RNNs) have shown promising performance for la...
11/23/2016 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Semantic Compositional Networks for Visual Captioning

A Semantic Compositional Network (SCN) is developed for image captioning...
11/23/2016 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Learning Generic Sentence Representations Using Convolutional Neural Networks

We propose a new encoder-decoder approach to learn distributed sentence ...
11/23/2016 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Adaptive Feature Abstraction for Translating Video to Text

Previous models for video captioning often use the output from a specifi...
11/23/2016 ∙ by Yunchen Pu, et al. ∙ 0 ∙ share

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Topic Compositional Neural Language Model

We propose a Topic Compositional Neural Language Model (TCNLM), a novel ...
12/28/2017 ∙ by Wenlin Wang, et al. ∙ 0 ∙ share

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Multi-Label Learning from Medical Plain Text with Convolutional Residual Models

Predicting diagnoses from Electronic Health Records (EHRs) is an importa...
01/15/2018 ∙ by Xinyuan Zhang, et al. ∙ 0 ∙ share

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Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation

We propose a hierarchically structured reinforcement learning approach t...
05/21/2018 ∙ by Qiuyuan Huang, et al. ∙ 0 ∙ share

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JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

A new generative adversarial network is developed for joint distribution...
06/08/2018 ∙ by Yunchen Pu, et al. ∙ 0 ∙ share

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Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

Responses generated by neural conversational models tend to lack informa...
09/16/2018 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Adversarial Text Generation via Feature-Mover's Distance

Generative adversarial networks (GANs) have achieved significant success...
09/17/2018 ∙ by Liqun Chen, et al. ∙ 0 ∙ share

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Sequence Generation with Guider Network

Sequence generation with reinforcement learning (RL) has received signif...
11/02/2018 ∙ by Ruiyi Zhang, et al. ∙ 0 ∙ share

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StoryGAN: A Sequential Conditional GAN for Story Visualization

In this work we propose a new task called Story Visualization. Given a m...
12/06/2018 ∙ by Yitong Li, et al. ∙ 0 ∙ share

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Sequential Attention GAN for Interactive Image Editing via Dialogue

In this paper, we introduce a new task - interactive image editing via c...
12/20/2018 ∙ by Yu Cheng, et al. ∙ 0 ∙ share

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Improving Sequence-to-Sequence Learning via Optimal Transport

Sequence-to-sequence models are commonly trained via maximum likelihood ...
01/18/2019 ∙ by Liqun Chen, et al. ∙ 0 ∙ share

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Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog

This paper presents Recurrent Dual Attention Network (ReDAN) for visual ...
02/01/2019 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Topic-Guided Variational Autoencoders for Text Generation

We propose a topic-guided variational autoencoder (TGVAE) model for text...
03/17/2019 ∙ by Wenlin Wang, et al. ∙ 0 ∙ share

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Relation-aware Graph Attention Network for Visual Question Answering

In order to answer semantically-complicated questions about an image, a ...
03/29/2019 ∙ by Linjie Li, et al. ∙ 0 ∙ share

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Domain Adaptive Text Style Transfer

Text style transfer without parallel data has achieved some practical su...
08/25/2019 ∙ by Dianqi Li, et al. ∙ 0 ∙ share

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Patient Knowledge Distillation for BERT Model Compression

Pre-trained language models such as BERT have proven to be highly effect...
08/25/2019 ∙ by Siqi Sun, et al. ∙ 0 ∙ share

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Adversarial Domain Adaptation for Machine Reading Comprehension

In this paper, we focus on unsupervised domain adaptation for Machine Re...
08/24/2019 ∙ by Huazheng Wang, et al. ∙ 0 ∙ share

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Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Recent unsupervised approaches to domain adaptation primarily focus on m...
09/11/2019 ∙ by Shuyang Dai, et al. ∙ 0 ∙ share

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TIGEr: Text-to-Image Grounding for Image Caption Evaluation

This paper presents a new metric called TIGEr for the automatic evaluati...
09/04/2019 ∙ by Ming Jiang, et al. ∙ 0 ∙ share

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What Makes A Good Story? Designing Composite Rewards for Visual Storytelling

Previous storytelling approaches mostly focused on optimizing traditiona...
09/11/2019 ∙ by Junjie Hu, et al. ∙ 0 ∙ share

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FreeLB: Enhanced Adversarial Training for Language Understanding

Adversarial training, which minimizes the maximal risk for label-preserv...
09/25/2019 ∙ by Chen Zhu, et al. ∙ 0 ∙ share

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UNITER: Learning UNiversal Image-TExt Representations

Joint image-text embedding is the bedrock for most Vision-and-Language (...
09/25/2019 ∙ by Yen-Chun Chen, et al. ∙ 0 ∙ share

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Meta Module Network for Compositional Visual Reasoning

There are two main lines of research on visual reasoning: neural module ...
10/08/2019 ∙ by Wenhu Chen, et al. ∙ 0 ∙ share

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Improving Textual Network Learning with Variational Homophilic Embeddings

The performance of many network learning applications crucially hinges o...
09/30/2019 ∙ by Wenlin Wang, et al. ∙ 0 ∙ share

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Discourse-Aware Neural Extractive Model for Text Summarization

Recently BERT has been adopted in state-of-the-art text summarization mo...
10/30/2019 ∙ by Jiacheng Xu, et al. ∙ 0 ∙ share

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Hierarchical Graph Network for Multi-hop Question Answering

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop...
11/09/2019 ∙ by Yuwei Fang, et al. ∙ 0 ∙ share

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Distilling the Knowledge of BERT for Text Generation

Large-scale pre-trained language model, such as BERT, has recently achie...
11/10/2019 ∙ by Yen-Chun Chen, et al. ∙ 0 ∙ share

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Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

We propose a novel graph-driven generative model, that unifies multiple ...
11/20/2019 ∙ by Wenlin Wang, et al. ∙ 0 ∙ share

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Nested-Wasserstein Self-Imitation Learning for Sequence Generation

Reinforcement learning (RL) has been widely studied for improving sequen...
01/20/2020 ∙ by Ruiyi Zhang, et al. ∙ 0 ∙ share

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Contextual Text Style Transfer

We introduce a new task, Contextual Text Style Transfer - translating a ...
04/30/2020 ∙ by Yu Cheng, et al. ∙ 0 ∙ share

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APo-VAE: Text Generation in Hyperbolic Space

Natural language often exhibits inherent hierarchical structure ingraine...
04/30/2020 ∙ by Shuyang Dai, et al. ∙ 0 ∙ share

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Zhe Gan

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