Shuyang Dai

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Duke University

Bridging Maximum Likelihood and Adversarial Learning via α-Divergence

Maximum likelihood (ML) and adversarial learning are two popular approac...
07/13/2020 ∙ by Miaoyun Zhao, et al. ∙ 0 ∙ share

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CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

Mutual information (MI) minimization has gained considerable interests i...
06/22/2020 ∙ by Pengyu Cheng, et al. ∙ 38 ∙ 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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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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Adaptation Across Extreme Variations using Unlabeled Domain Bridges

We tackle an unsupervised domain adaptation problem for which the domain...
06/05/2019 ∙ by Shuyang Dai, et al. ∙ 4 ∙ 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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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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Symmetric Variational Autoencoder and Connections to Adversarial Learning

A new form of the variational autoencoder (VAE) is proposed, based on th...
09/06/2017 ∙ by Liqun Chen, et al. ∙ 0 ∙ share

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Shuyang Dai

Featured Co-authors

Lawrence Carin

Yu Cheng

Zhe Gan

Yizhe Zhang

Jingjing Liu

Chunyuan Li

Manmohan Chandraker

Ricardo Henao

Dinghan Shen

Guoyin Wang

Yi-Hsuan Tsai

Bridging Maximum Likelihood and Adversarial Learning via α-Divergence

CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

APo-VAE: Text Generation in Hyperbolic Space

Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Adaptation Across Extreme Variations using Unlabeled Domain Bridges

Adversarial Text Generation via Feature-Mover's Distance

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

Symmetric Variational Autoencoder and Connections to Adversarial Learning