190 citations · 1.2k across the 77 of their papers we have counts for
8 papers · 2 filters
Diversity-Promoting Bayesian Learning of Latent Variable Models
Pengtao Xie, Jun Zhu, Eric P. Xing
To address three important issues involved in latent variable models (LVMs), including capturing infrequent patterns, achieving small-sized but expressive models and alleviating ov…
Structured Generative Adversarial Networks
Zhijie Deng, Hao Zhang, Xiaodan Liang +4
We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labele…
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning
Yucen Luo, Jun Zhu, Mengxi Li +2
The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under diff…
Boosting Adversarial Attacks with Momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang +4
Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve…
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Yichi Zhou, Jun Zhu, Jingwei Zhuo
Thompson sampling has impressive empirical performance for many multi-armed bandit problems. But current algorithms for Thompson sampling only work for the case of conjugate priors…
Towards Robust Detection of Adversarial Examples
Tianyu Pang, Chao Du, Yinpeng Dong +1
Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples. In this paper, we present a novel training p…