224 citations · 3.2k across the 170 of their papers we have counts for
21 papers · 1 filter
Learning Random Fourier Features by Hybrid Constrained Optimization
Jianqiao Wangni, Jingwei Zhuo, Jun Zhu
The kernel embedding algorithm is an important component for adapting kernel methods to large datasets. Since the algorithm consumes a major computation cost in the testing phase,…
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Danyang Sun, Tongzheng Ren, Chongxun Li +2
Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities. In this paper, we propose a novel framework named Style-Awa…
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong +3
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoise…
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…
Visual Concepts and Compositional Voting
Jianyu Wang, Zhishuai Zhang, Cihang Xie +5
It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blo…
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…
Message Passing Stein Variational Gradient Descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi +3
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation…
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…
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le Song
Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes the representation of a node recursively from its neighbors,…
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…
ZhuSuan: A Library for Bayesian Deep Learning
Jiaxin Shi, Jianfei Chen, Jun Zhu +4
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep…
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
Yinpeng Dong, Hang Su, Jun Zhu +1
Deep neural networks (DNNs) have demonstrated impressive performance on a wide array of tasks, but they are usually considered opaque since internal structure and learned parameter…
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…
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
Yinpeng Dong, Renkun Ni, Jianguo Li +3
Low-bit deep neural networks (DNNs) become critical for embedded applications due to their low storage requirement and computing efficiency. However, they suffer much from the non-…
Detecting Semantic Parts on Partially Occluded Objects
Jianyu Wang, Cihang Xie, Zhishuai Zhang +3
In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested…
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…
Kernel Implicit Variational Inference
Jiaxin Shi, Shengyang Sun, Jun Zhu
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distr…
Improving Interpretability of Deep Neural Networks with Semantic Information
Yinpeng Dong, Hang Su, Jun Zhu +1
Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of ho…
Triple Generative Adversarial Nets
Chongxuan Li, Kun Xu, Jun Zhu +1
Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and…
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Jianfei Chen, Jun Zhu, Jie Lu +1
Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical stru…
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Shixia Liu, Xiting Wang, Mengchen Liu +1
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users…