Jun Zhu

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Qualcomm ∙ University of Wisconsin-Madison ∙ Carnegie Mellon University

Associate Professor, Computer Science Department at Tsinghua University. Adjunct Faculty, Machine Learning Department at Carnegie Mellon University.

Triple Generative Adversarial Networks

Generative adversarial networks (GANs) have shown promise in image gener...
12/20/2019 ∙ by Chongxuan Li, et al. ∙ 28 ∙ share

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Function Space Particle Optimization for Bayesian Neural Networks

While Bayesian neural networks (BNNs) have drawn increasing attention, t...
02/26/2019 ∙ by Ziyu Wang, et al. ∙ 24 ∙ share

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Adversarial Variational Inference and Learning in Markov Random Fields

Markov random fields (MRFs) find applications in a variety of machine le...
01/24/2019 ∙ by Chongxuan Li, et al. ∙ 18 ∙ share

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DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures

Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...
11/22/2019 ∙ by Zhijie Deng, et al. ∙ 13 ∙ share

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Boosting Adversarial Training with Hypersphere Embedding

Adversarial training (AT) is one of the most effective defenses to impro...
02/20/2020 ∙ by Tianyu Pang, et al. ∙ 13 ∙ share

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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Score matching provides an effective approach to learning flexible unnor...
02/18/2020 ∙ by Ziyu Wang, et al. ∙ 9 ∙ share

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Deep Structured Generative Models

Deep generative models have shown promising results in generating realis...
07/10/2018 ∙ by Kun Xu, et al. ∙ 8 ∙ share

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Transferable Adversarial Attacks for Image and Video Object Detection

Adversarial examples have been demonstrated to threaten many computer vi...
11/30/2018 ∙ by Xingxing Wei, et al. ∙ 8 ∙ share

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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Standard variational lower bounds used to train latent variable models p...
04/01/2020 ∙ by Yucen Luo, et al. ∙ 8 ∙ share

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Analyzing the Noise Robustness of Deep Neural Networks

Deep neural networks (DNNs) are vulnerable to maliciously generated adve...
10/09/2018 ∙ by Mengchen Liu, et al. ∙ 6 ∙ share

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Towards Privacy Protection by Generating Adversarial Identity Masks

As billions of personal data such as photos are shared through social me...
03/15/2020 ∙ by Xiao Yang, et al. ∙ 5 ∙ share

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Diversity-Promoting Bayesian Learning of Latent Variable Models

To address three important issues involved in latent variable models (LV...
11/23/2017 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

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Visual Concepts and Compositional Voting

It is very attractive to formulate vision in terms of pattern theory Mum...
11/13/2017 ∙ by Jianyu Wang, et al. ∙ 0 ∙ share

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Stochastic Training of Graph Convolutional Networks

Graph convolutional networks (GCNs) are powerful deep neural networks fo...
10/29/2017 ∙ by Jianfei Chen, et al. ∙ 0 ∙ share

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Structured Generative Adversarial Networks

We study the problem of conditional generative modeling based on designa...
11/02/2017 ∙ by Zhijie Deng, et al. ∙ 0 ∙ share

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Boosting Adversarial Attacks with Momentum

Deep neural networks are vulnerable to adversarial examples, which poses...
10/17/2017 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors

Thompson sampling has impressive empirical performance for many multi-ar...
08/16/2017 ∙ by Yichi Zhou, et al. ∙ 0 ∙ share

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Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

Compared with artificial neural networks (ANNs), spiking neural networks...
06/08/2017 ∙ by Yujie Wu, et al. ∙ 0 ∙ share

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Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization

Low-bit deep neural networks (DNNs) become critical for embedded applica...
08/03/2017 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Implicit Variational Inference with Kernel Density Ratio Fitting

Recent progress in variational inference has paid much attention to the ...
05/29/2017 ∙ by Jiaxin Shi, et al. ∙ 0 ∙ share

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Detecting Semantic Parts on Partially Occluded Objects

In this paper, we address the task of detecting semantic parts on partia...
07/25/2017 ∙ by Jianyu Wang, et al. ∙ 0 ∙ share

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SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data

Understanding how brain functions has been an intriguing topic for years...
11/29/2016 ∙ by Jingkang Yang, et al. ∙ 0 ∙ share

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The YouTube-8M Kaggle Competition: Challenges and Methods

We took part in the YouTube-8M Video Understanding Challenge hosted on K...
06/28/2017 ∙ by Haosheng Zou, et al. ∙ 0 ∙ share

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Scalable Inference for Nested Chinese Restaurant Process Topic Models

Nested Chinese Restaurant Process (nCRP) topic models are powerful nonpa...
02/23/2017 ∙ by Jianfei Chen, et al. ∙ 0 ∙ share

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Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective

Interactive model analysis, the process of understanding, diagnosing, an...
02/04/2017 ∙ by Shixia Liu, et al. ∙ 0 ∙ share

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SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discre...
10/08/2016 ∙ by Kaiwei Li, et al. ∙ 0 ∙ share

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Improving Interpretability of Deep Neural Networks with Semantic Information

Interpretability of deep neural networks (DNNs) is essential since it en...
03/12/2017 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Triple Generative Adversarial Nets

Generative Adversarial Nets (GANs) have shown promise in image generatio...
03/07/2017 ∙ by Chongxuan Li, et al. ∙ 0 ∙ share

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Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization

Bayesian matrix completion has been studied based on a low-rank matrix f...
12/03/2015 ∙ by Yang Song, et al. ∙ 0 ∙ share

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Riemannian Stein Variational Gradient Descent for Bayesian Inference

We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Baye...
11/30/2017 ∙ by Chang Liu, et al. ∙ 0 ∙ share

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Kernel Bayesian Inference with Posterior Regularization

We propose a vector-valued regression problem whose solution is equivale...
07/07/2016 ∙ by Yang Song, et al. ∙ 0 ∙ share

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Max-Margin Deep Generative Models for (Semi-)Supervised Learning

Deep generative models (DGMs) are effective on learning multilayered rep...
11/22/2016 ∙ by Chongxuan Li, et al. ∙ 0 ∙ share

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Max-Margin Nonparametric Latent Feature Models for Link Prediction

Link prediction is a fundamental task in statistical network analysis. R...
02/24/2016 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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Scaling up Dynamic Topic Models

Dynamic topic models (DTMs) are very effective in discovering topics and...
02/19/2016 ∙ by Arnab Bhadury, et al. ∙ 0 ∙ share

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Streaming Gibbs Sampling for LDA Model

Streaming variational Bayes (SVB) is successful in learning LDA models i...
01/06/2016 ∙ by Yang Gao, et al. ∙ 0 ∙ share

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Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational mode...
12/07/2015 ∙ by Bei Chen, et al. ∙ 0 ∙ share

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WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation

Developing efficient and scalable algorithms for Latent Dirichlet Alloca...
10/29/2015 ∙ by Jianfei Chen, et al. ∙ 0 ∙ share

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Fast Sampling for Bayesian Max-Margin Models

Bayesian max-margin models have shown superiority in various practical a...
04/27/2015 ∙ by Wenbo Hu, et al. ∙ 0 ∙ share

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Towards Better Analysis of Deep Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have achieved breakthrough per...
04/24/2016 ∙ by Mengchen Liu, et al. ∙ 0 ∙ share

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Big Learning with Bayesian Methods

Explosive growth in data and availability of cheap computing resources h...
11/24/2014 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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Learning to Generate with Memory

Memory units have been widely used to enrich the capabilities of deep ne...
02/24/2016 ∙ by Chongxuan Li, et al. ∙ 0 ∙ share

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Gibbs Max-margin Topic Models with Data Augmentation

Max-margin learning is a powerful approach to building classifiers and s...
10/10/2013 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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Discriminative Relational Topic Models

Many scientific and engineering fields involve analyzing network data. F...
10/09/2013 ∙ by Ning Chen, et al. ∙ 0 ∙ share

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Improved Bayesian Logistic Supervised Topic Models with Data Augmentation

Supervised topic models with a logistic likelihood have two issues that ...
10/09/2013 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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Max-margin Deep Generative Models

Deep generative models (DGMs) are effective on learning multilayered rep...
04/26/2015 ∙ by Chongxuan Li, et al. ∙ 0 ∙ share

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Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs

Existing Bayesian models, especially nonparametric Bayesian methods, rel...
10/05/2012 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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Sparse Topical Coding

We present sparse topical coding (STC), a non-probabilistic formulation ...
02/14/2012 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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MedLDA: A General Framework of Maximum Margin Supervised Topic Models

Supervised topic models utilize document's side information for discover...
12/30/2009 ∙ by Jun Zhu, et al. ∙ 0 ∙ share

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PSDVec: a Toolbox for Incremental and Scalable Word Embedding

PSDVec is a Python/Perl toolbox that learns word embeddings, i.e. the ma...
06/10/2016 ∙ by Shaohua Li, et al. ∙ 0 ∙ share

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Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)

Word embedding maps words into a low-dimensional continuous embedding sp...
06/09/2016 ∙ by Shaohua Li, et al. ∙ 0 ∙ share

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Jun Zhu

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