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Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

May 25, 2019
Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu


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Improving Adversarial Robustness via Promoting Ensemble Diversity

Jan 25, 2019
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu


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Adversarial Variational Inference and Learning in Markov Random Fields

Jan 24, 2019
Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang


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Learning Implicit Generative Models by Teaching Explicit Ones

Jul 10, 2018
Chao Du, Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang


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Collaborative Filtering with User-Item Co-Autoregressive Models

Jul 05, 2018
Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang

* Published in AAAI 2018 

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Max-Mahalanobis Linear Discriminant Analysis Networks

Jun 19, 2018
Tianyu Pang, Chao Du, Jun Zhu


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Towards Robust Detection of Adversarial Examples

Feb 26, 2018
Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu


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Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing

Jul 20, 2017
Mohammed A. Zidan, YeonJoo Jeong, Jong Hong Shin, Chao Du, Zhengya Zhang, Wei D. Lu


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Learning Deep Generative Models with Doubly Stochastic MCMC

Mar 07, 2016
Chao Du, Jun Zhu, Bo Zhang


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Inner Product Similarity Search using Compositional Codes

Jun 20, 2014
Chao Du, Jingdong Wang

* The approach presented in this paper (ECCV14 submission) is closely related to multi-stage vector quantization and residual quantization. Thanks the reviewers (CVPR14 and ECCV14) for pointing out the relationship to the two algorithms. Related paper: http://sites.skoltech.ru/app/data/uploads/sites/2/2013/09/CVPR14.pdf, which also adopts the summation of vectors for vector approximation 

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