
Triple Generative Adversarial Networks
Generative adversarial networks (GANs) have shown promise in image gener...
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Function Space Particle Optimization for Bayesian Neural Networks
While Bayesian neural networks (BNNs) have drawn increasing attention, t...
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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...
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DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...
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Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses to impro...
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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Score matching provides an effective approach to learning flexible unnor...
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Deep Structured Generative Models
Deep generative models have shown promising results in generating realis...
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Transferable Adversarial Attacks for Image and Video Object Detection
Adversarial examples have been demonstrated to threaten many computer vi...
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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...
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Analyzing the Noise Robustness of Deep Neural Networks
Deep neural networks (DNNs) are vulnerable to maliciously generated adve...
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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...
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DiversityPromoting Bayesian Learning of Latent Variable Models
To address three important issues involved in latent variable models (LV...
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Visual Concepts and Compositional Voting
It is very attractive to formulate vision in terms of pattern theory Mum...
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Stochastic Training of Graph Convolutional Networks
Graph convolutional networks (GCNs) are powerful deep neural networks fo...
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Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designa...
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Boosting Adversarial Attacks with Momentum
Deep neural networks are vulnerable to adversarial examples, which poses...
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Racing Thompson: an Efficient Algorithm for Thompson Sampling with Nonconjugate Priors
Thompson sampling has impressive empirical performance for many multiar...
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SpatioTemporal Backpropagation for Training Highperformance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
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Learning Accurate LowBit Deep Neural Networks with Stochastic Quantization
Lowbit deep neural networks (DNNs) become critical for embedded applica...
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Implicit Variational Inference with Kernel Density Ratio Fitting
Recent progress in variational inference has paid much attention to the ...
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Detecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partia...
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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...
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The YouTube8M Kaggle Competition: Challenges and Methods
We took part in the YouTube8M Video Understanding Challenge hosted on K...
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Scalable Inference for Nested Chinese Restaurant Process Topic Models
Nested Chinese Restaurant Process (nCRP) topic models are powerful nonpa...
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Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Interactive model analysis, the process of understanding, diagnosing, an...
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SaberLDA: SparsityAware Learning of Topic Models on GPUs
Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discre...
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Improving Interpretability of Deep Neural Networks with Semantic Information
Interpretability of deep neural networks (DNNs) is essential since it en...
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Triple Generative Adversarial Nets
Generative Adversarial Nets (GANs) have shown promise in image generatio...
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Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization
Bayesian matrix completion has been studied based on a lowrank matrix f...
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Riemannian Stein Variational Gradient Descent for Bayesian Inference
We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Baye...
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Kernel Bayesian Inference with Posterior Regularization
We propose a vectorvalued regression problem whose solution is equivale...
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MaxMargin Deep Generative Models for (Semi)Supervised Learning
Deep generative models (DGMs) are effective on learning multilayered rep...
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MaxMargin Nonparametric Latent Feature Models for Link Prediction
Link prediction is a fundamental task in statistical network analysis. R...
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Scaling up Dynamic Topic Models
Dynamic topic models (DTMs) are very effective in discovering topics and...
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Streaming Gibbs Sampling for LDA Model
Streaming variational Bayes (SVB) is successful in learning LDA models i...
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Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
We present a discriminative nonparametric latent feature relational mode...
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WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation
Developing efficient and scalable algorithms for Latent Dirichlet Alloca...
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Fast Sampling for Bayesian MaxMargin Models
Bayesian maxmargin models have shown superiority in various practical a...
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Towards Better Analysis of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have achieved breakthrough per...
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Big Learning with Bayesian Methods
Explosive growth in data and availability of cheap computing resources h...
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Learning to Generate with Memory
Memory units have been widely used to enrich the capabilities of deep ne...
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Gibbs Maxmargin Topic Models with Data Augmentation
Maxmargin learning is a powerful approach to building classifiers and s...
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Discriminative Relational Topic Models
Many scientific and engineering fields involve analyzing network data. F...
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Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Supervised topic models with a logistic likelihood have two issues that ...
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Maxmargin Deep Generative Models
Deep generative models (DGMs) are effective on learning multilayered rep...
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Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
Existing Bayesian models, especially nonparametric Bayesian methods, rel...
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Sparse Topical Coding
We present sparse topical coding (STC), a nonprobabilistic formulation ...
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MedLDA: A General Framework of Maximum Margin Supervised Topic Models
Supervised topic models utilize document's side information for discover...
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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...
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Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Word embedding maps words into a lowdimensional continuous embedding sp...
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Jun Zhu
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Associate Professor, Computer Science Department at Tsinghua University. Adjunct Faculty, Machine Learning Department at Carnegie Mellon University.