b'Mingyuan Zhou'

research

∙ 08/05/2022

Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification

Imbalanced data pose challenges for deep learning based classification m...

Dandan Guo, et al. ∙

research

∙ 06/15/2022

CARD: Classification and Regression Diffusion Models

Learning the distribution of a continuous or categorical response variab...

Xizewen Han, et al. ∙

research

∙ 06/14/2022

Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

Offline reinforcement learning (RL) extends the paradigm of classical RL...

Shentao Yang, et al. ∙

research

∙ 06/14/2022

Probabilistic Conformal Prediction Using Conditional Random Samples

This paper proposes probabilistic conformal prediction (PCP), a predicti...

Zhendong Wang, et al. ∙

research

∙ 06/05/2022

Diffusion-GAN: Training GANs with Diffusion

For stable training of generative adversarial networks (GANs), injecting...

Zhendong Wang, et al. ∙

research

∙ 05/10/2022

ALLSH: Active Learning Guided by Local Sensitivity and Hardness

Active learning, which effectively collects informative unlabeled data f...

Shujian Zhang, et al. ∙

research

∙ 03/03/2022

Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings

A topic model is often formulated as a generative model that explains ho...

Dongsheng Wang, et al. ∙

research

∙ 02/19/2022

A Regularized Implicit Policy for Offline Reinforcement Learning

Offline reinforcement learning enables learning from a fixed dataset, wi...

Shentao Yang, et al. ∙

research

∙ 02/19/2022

Truncated Diffusion Probabilistic Models

Employing a forward Markov diffusion chain to gradually map the data to ...

Huangjie Zheng, et al. ∙

research

∙ 02/14/2022

Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs

Token-mixing multi-layer perceptron (MLP) models have shown competitive ...

Huangjie Zheng, et al. ∙

research

∙ 12/15/2021

Bayesian Graph Contrastive Learning

Contrastive learning has become a key component of self-supervised learn...

Arman Hasanzadeh, et al. ∙

research

∙ 10/27/2021

TopicNet: Semantic Graph-Guided Topic Discovery

Existing deep hierarchical topic models are able to extract semantically...

Zhibin Duan, et al. ∙

research

∙ 10/26/2021

CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

Accurately backpropagating the gradient through categorical variables is...

Alek Dimitriev, et al. ∙

research

∙ 10/25/2021

Alignment Attention by Matching Key and Query Distributions

The neural attention mechanism has been incorporated into deep neural ne...

Shujian Zhang, et al. ∙

research

∙ 10/18/2021

Learning Prototype-oriented Set Representations for Meta-Learning

Learning from set-structured data is a fundamental problem that has rece...

Dandan Guo, et al. ∙

research

∙ 06/30/2021

Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network

Hierarchical topic models such as the gamma belief network (GBN) have de...

Zhibin Duan, et al. ∙

research

∙ 06/15/2021

Probabilistic Margins for Instance Reweighting in Adversarial Training

Reweighting adversarial data during training has been recently shown to ...

Qizhou Wang, et al. ∙

research

∙ 06/09/2021

Bayesian Attention Belief Networks

Attention-based neural networks have achieved state-of-the-art results o...

Shujian Zhang, et al. ∙

research

∙ 06/01/2021

Adversarially Adaptive Normalization for Single Domain Generalization

Single domain generalization aims to learn a model that performs well on...

Xinjie Fan, et al. ∙

research

∙ 05/28/2021

ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables

Estimating the gradients for binary variables is a task that arises freq...

Alek Dimitriev, et al. ∙

research

∙ 05/10/2021

Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning

Observing a set of images and their corresponding paragraph-captions, a ...

Dandan Guo, et al. ∙

research

∙ 04/02/2021

Partition-Guided GANs

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

Mohammadreza Armandpour, et al. ∙

research

∙ 03/06/2021

Contextual Dropout: An Efficient Sample-Dependent Dropout Module

Dropout has been demonstrated as a simple and effective module to not on...

Xinjie Fan, et al. ∙

research

∙ 12/28/2020

Comparing Probability Distributions with Conditional Transport

To measure the difference between two probability distributions, we prop...

Huangjie Zheng, et al. ∙

research

∙ 12/25/2020

Self-supervised Pre-training with Hard Examples Improves Visual Representations

Self-supervised pre-training (SSP) employs random image transformations ...

Chunyuan Li, et al. ∙

research

∙ 10/31/2020

Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference

Efficient modeling of relational data arising in physical, social, and i...

Ali Lotfi Rezaabad, et al. ∙

research

∙ 10/21/2020

Convex Polytope Trees

A decision tree is commonly restricted to use a single hyperplane to spl...

Mohammadreza Armandpour, et al. ∙

research

∙ 10/20/2020

Bayesian Attention Modules

Attention modules, as simple and effective tools, have not only enabled ...

Xinjie Fan, et al. ∙

research

∙ 10/02/2020

MCMC-Interactive Variational Inference

Leveraging well-established MCMC strategies, we propose MCMC-interactive...

Quan Zhang, et al. ∙

research

∙ 09/28/2020

Variational Temporal Deep Generative Model for Radar HRRP Target Recognition

We develop a recurrent gamma belief network (rGBN) for radar automatic t...

Dandan Guo, et al. ∙

research

∙ 07/25/2020

Graph Gamma Process Generalized Linear Dynamical Systems

We introduce graph gamma process (GGP) linear dynamical systems to model...

Rahi Kalantari, et al. ∙

research

∙ 07/13/2020

Implicit Distributional Reinforcement Learning

To improve the sample efficiency of policy-gradient based reinforcement ...

Yuguang Yue, et al. ∙

research

∙ 06/11/2020

Probabilistic Best Subset Selection via Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...

Mingzhang Yin, et al. ∙

research

∙ 06/11/2020

Probabilistic Best Subset Selection by Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...

Mingzhang Yin, et al. ∙

research

∙ 06/07/2020

Bayesian Graph Neural Networks with Adaptive Connection Sampling

We propose a unified framework for adaptive connection sampling in graph...

Arman Hasanzadeh, et al. ∙

research

∙ 05/21/2020

Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator

Semantic hashing has become a crucial component of fast similarity searc...

Siamak Zamani Dadaneh, et al. ∙

research

∙ 05/03/2020

Mutual Information Gradient Estimation for Representation Learning

Mutual Information (MI) plays an important role in representation learni...

Liangjian Wen, et al. ∙

research

∙ 02/12/2020

Learnable Bernoulli Dropout for Bayesian Deep Learning

In this work, we propose learnable Bernoulli dropout (LBD), a new model-...

Shahin Boluki, et al. ∙

research

∙ 12/31/2019

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

Sequence generation models are commonly refined with reinforcement learn...

Xinjie Fan, et al. ∙

research

∙ 12/21/2019

Recurrent Hierarchical Topic-Guided Neural Language Models

To simultaneously capture syntax and global semantics from a text corpus...

Dandan Guo, et al. ∙

research

∙ 12/09/2019

Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critic...

Mingzhang Yin, et al. ∙

research

∙ 11/02/2019

Weibull Racing Time-to-event Modeling and Analysis of Online Borrowers' Loan Payoff and Default

We propose Weibull delegate racing (WDR) to explicitly model surviving u...

Quan Zhang, et al. ∙

research

∙ 11/01/2019

ARSM Gradient Estimator for Supervised Learning to Rank

We propose a new model for supervised learning to rank. In our model, th...

Siamak Zamani Dadaneh, et al. ∙

research

∙ 10/30/2019

Thompson Sampling via Local Uncertainty

Thompson sampling is an efficient algorithm for sequential decision maki...

Zhendong Wang, et al. ∙

research

∙ 10/28/2019

Poisson-Randomized Gamma Dynamical Systems

This paper presents the Poisson-randomized gamma dynamical system (PRGDS...

Aaron Schein, et al. ∙

research

∙ 10/28/2019

Semi-Implicit Stochastic Recurrent Neural Networks

Stochastic recurrent neural networks with latent random variables of com...

Ehsan Hajiramezanali, et al. ∙

research

∙ 10/18/2019

Semi-supervised Learning using Adversarial Training with Good and Bad Samples

In this work, we investigate semi-supervised learning (SSL) for image cl...

Wenyuan Li, et al. ∙

research

∙ 08/26/2019

Variational Graph Recurrent Neural Networks

Representation learning over graph structured data has been mostly studi...

Ehsan Hajiramezanali, et al. ∙

research

∙ 08/19/2019

Semi-Implicit Graph Variational Auto-Encoders

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to ex...

Arman Hasanzadeh, et al. ∙

research

∙ 08/01/2019

Bayesian Gamma-Negative Binomial Modeling of Single-Cell RNA Sequencing Data

Background: Single-cell RNA sequencing (scRNA-seq) is a powerful profili...

Siamak Zamani Dadaneh, et al. ∙

Mingyuan Zhou

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