
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Hierarchical topic models such as the gamma belief network (GBN) have de...
read it

Probabilistic Margins for Instance Reweighting in Adversarial Training
Reweighting adversarial data during training has been recently shown to ...
read it

Bayesian Attention Belief Networks
Attentionbased neural networks have achieved stateoftheart results o...
read it

Adversarially Adaptive Normalization for Single Domain Generalization
Single domain generalization aims to learn a model that performs well on...
read it

ARMS: AntitheticREINFORCEMultiSample Gradient for Binary Variables
Estimating the gradients for binary variables is a task that arises freq...
read it

Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning
Observing a set of images and their corresponding paragraphcaptions, a ...
read it

PartitionGuided GANs
Despite the success of Generative Adversarial Networks (GANs), their tra...
read it

Contextual Dropout: An Efficient SampleDependent Dropout Module
Dropout has been demonstrated as a simple and effective module to not on...
read it

Comparing Probability Distributions with Conditional Transport
To measure the difference between two probability distributions, we prop...
read it

Selfsupervised Pretraining with Hard Examples Improves Visual Representations
Selfsupervised pretraining (SSP) employs random image transformations ...
read it

Hyperbolic Graph Embedding with Enhanced SemiImplicit Variational Inference
Efficient modeling of relational data arising in physical, social, and i...
read it

Convex Polytope Trees
A decision tree is commonly restricted to use a single hyperplane to spl...
read it

Bayesian Attention Modules
Attention modules, as simple and effective tools, have not only enabled ...
read it

MCMCInteractive Variational Inference
Leveraging wellestablished MCMC strategies, we propose MCMCinteractive...
read it

Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
We develop a recurrent gamma belief network (rGBN) for radar automatic t...
read it

Graph Gamma Process Generalized Linear Dynamical Systems
We introduce graph gamma process (GGP) linear dynamical systems to model...
read it

Implicit Distributional Reinforcement Learning
To improve the sample efficiency of policygradient based reinforcement ...
read it

Probabilistic Best Subset Selection via GradientBased Optimization
In highdimensional statistics, variable selection is an optimization pr...
read it

Probabilistic Best Subset Selection by GradientBased Optimization
In highdimensional statistics, variable selection is an optimization pr...
read it

Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph...
read it

Pairwise Supervised Hashing with Bernoulli Variational AutoEncoder and SelfControl Gradient Estimator
Semantic hashing has become a crucial component of fast similarity searc...
read it

Mutual Information Gradient Estimation for Representation Learning
Mutual Information (MI) plays an important role in representation learni...
read it

Learnable Bernoulli Dropout for Bayesian Deep Learning
In this work, we propose learnable Bernoulli dropout (LBD), a new model...
read it

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Sequence generation models are commonly refined with reinforcement learn...
read it

Recurrent Hierarchical TopicGuided Neural Language Models
To simultaneously capture syntax and global semantics from a text corpus...
read it

MetaLearning without Memorization
The ability to learn new concepts with small amounts of data is a critic...
read it

Weibull Racing Timetoevent Modeling and Analysis of Online Borrowers' Loan Payoff and Default
We propose Weibull delegate racing (WDR) to explicitly model surviving u...
read it

ARSM Gradient Estimator for Supervised Learning to Rank
We propose a new model for supervised learning to rank. In our model, th...
read it

Thompson Sampling via Local Uncertainty
Thompson sampling is an efficient algorithm for sequential decision maki...
read it

PoissonRandomized Gamma Dynamical Systems
This paper presents the Poissonrandomized gamma dynamical system (PRGDS...
read it

SemiImplicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of com...
read it

Semisupervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semisupervised learning (SSL) for image cl...
read it

Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studi...
read it

SemiImplicit Graph Variational AutoEncoders
Semiimplicit graph variational autoencoder (SIGVAE) is proposed to ex...
read it

Bayesian GammaNegative Binomial Modeling of SingleCell RNA Sequencing Data
Background: Singlecell RNA sequencing (scRNAseq) is a powerful profili...
read it

SemiImplicit Generative Model
To combine explicit and implicit generative models, we introduce semiim...
read it

Variational HeteroEncoder Randomized Generative Adversarial Networks for Joint ImageText Modeling
For bidirectional joint imagetext modeling, we develop variational hete...
read it

Convolutional Poisson Gamma Belief Network
For text analysis, one often resorts to a lossy representation that eith...
read it

ARSM: AugmentREINFORCESwapMerge Estimator for Gradient Backpropagation Through Categorical Variables
To address the challenge of backpropagating the gradient through categor...
read it

Deep Generative Models for Sparse, Highdimensional, and Overdispersed Discrete Data
Many applications, such as text modelling, highthroughput sequencing, a...
read it

NonLambertian Surface Shape and Reflectance Reconstruction Using Concentric MultiSpectral Light Field
Recovering the shape and reflectance of nonLambertian surfaces remains ...
read it

3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
We present a new color photometric stereo (CPS) method that can recover ...
read it

AugmentReinforceMerge Policy Gradient for Binary Stochastic Policy
Due to the high variance of policy gradients, onpolicy optimization alg...
read it

Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing de...
read it

Deep Poisson gamma dynamical systems
We develop deep Poissongamma dynamical systems (DPGDS) to model sequent...
read it

Bayesian multidomain learning for cancer subtype discovery from nextgeneration sequencing count data
Precision medicine aims for personalized prognosis and therapeutics by u...
read it

Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
We propose Lomax delegate racing (LDR) to explicitly model the mechanism...
read it

ARM: AugmentREINFORCEMerge Gradient for Discrete Latent Variable Models
To backpropagate the gradients through discrete stochastic layers, we en...
read it

SemiImplicit Variational Inference
Semiimplicit variational inference (SIVI) is introduced to expand the c...
read it

Parsimonious Bayesian deep networks
Combining Bayesian nonparametrics and a forward model selection strategy...
read it
Mingyuan Zhou
is this you? claim profile
Assistant Professor of Statistics at The University of Texas at Austin