
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 ∙ shareread it

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 ∙ shareread it

Deep Structured Generative Models
Deep generative models have shown promising results in generating realis...
07/10/2018 ∙ by Kun Xu, et al. ∙ 8 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

DiversityPromoting 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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designa...
11/02/2017 ∙ by Zhijie Deng, et al. ∙ 0 ∙ shareread it

Boosting Adversarial Attacks with Momentum
Deep neural networks are vulnerable to adversarial examples, which poses...
10/17/2017 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ shareread it

Racing Thompson: an Efficient Algorithm for Thompson Sampling with Nonconjugate Priors
Thompson sampling has impressive empirical performance for many multiar...
08/16/2017 ∙ by Yichi Zhou, et al. ∙ 0 ∙ shareread it

SpatioTemporal Backpropagation for Training Highperformance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
06/08/2017 ∙ by Yujie Wu, et al. ∙ 0 ∙ shareread it

Learning Accurate LowBit Deep Neural Networks with Stochastic Quantization
Lowbit deep neural networks (DNNs) become critical for embedded applica...
08/03/2017 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

The YouTube8M Kaggle Competition: Challenges and Methods
We took part in the YouTube8M Video Understanding Challenge hosted on K...
06/28/2017 ∙ by Haosheng Zou, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

SaberLDA: SparsityAware 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 ∙ shareread it

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 ∙ shareread it

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

Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization
Bayesian matrix completion has been studied based on a lowrank matrix f...
12/03/2015 ∙ by Yang Song, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

Kernel Bayesian Inference with Posterior Regularization
We propose a vectorvalued regression problem whose solution is equivale...
07/07/2016 ∙ by Yang Song, et al. ∙ 0 ∙ shareread it

MaxMargin 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 ∙ shareread it

MaxMargin 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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

Fast Sampling for Bayesian MaxMargin Models
Bayesian maxmargin models have shown superiority in various practical a...
04/27/2015 ∙ by Wenbo Hu, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

Gibbs Maxmargin Topic Models with Data Augmentation
Maxmargin learning is a powerful approach to building classifiers and s...
10/10/2013 ∙ by Jun Zhu, et al. ∙ 0 ∙ shareread it

Discriminative Relational Topic Models
Many scientific and engineering fields involve analyzing network data. F...
10/09/2013 ∙ by Ning Chen, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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

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 ∙ shareread it

Sparse Topical Coding
We present sparse topical coding (STC), a nonprobabilistic formulation ...
02/14/2012 ∙ by Jun Zhu, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Word embedding maps words into a lowdimensional continuous embedding sp...
06/09/2016 ∙ by Shaohua Li, et al. ∙ 0 ∙ shareread it

Spectral Learning for Supervised Topic Models
Supervised topic models simultaneously model the latent topic structure ...
02/19/2016 ∙ by Yong Ren, et al. ∙ 0 ∙ shareread it

Jointly Modeling Topics and Intents with Global Order Structure
Modeling document structure is of great importance for discourse analysi...
12/07/2015 ∙ by Bei Chen, et al. ∙ 0 ∙ shareread it

Building Memory with Concept Learning Capabilities from Largescale Knowledge Base
We present a new perspective on neural knowledge base (KB) embeddings, f...
12/03/2015 ∙ by Jiaxin Shi, et al. ∙ 0 ∙ shareread it

A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Most existing word embedding methods can be categorized into Neural Embe...
08/16/2015 ∙ by Shaohua Li, et al. ∙ 0 ∙ shareread it

Defense against Adversarial Attacks Using HighLevel Representation Guided Denoiser
Neural networks are vulnerable to adversarial examples. This phenomenon ...
12/08/2017 ∙ by Fangzhou Liao, et al. ∙ 0 ∙ shareread it

Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Automatically writing stylized Chinese characters is an attractive yet c...
12/06/2017 ∙ by Danyang Sun, et al. ∙ 0 ∙ shareread it
Jun Zhu
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Associate Professor, Computer Science Department at Tsinghua University. Adjunct Faculty, Machine Learning Department at Carnegie Mellon University.