Search Results for author: Mingyuan Zhou

Found 86 papers, 39 papers with code

Friendly Topic Assistant for Transformer Based Abstractive Summarization

no code implementations EMNLP 2020 Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou

Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.

Abstractive Text Summarization Document Summarization +1

Learning Prototype-oriented Set Representations for Meta-Learning

no code implementations18 Oct 2021 Dandan Guo, Long Tian, Minghe Zhang, Mingyuan Zhou, Hongyuan Zha

Since our plug-and-play framework can be applied to many meta-learning problems, we further instantiate it to the cases of few-shot classification and implicit meta generative modeling.

Meta-Learning

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering

1 code implementation ACL 2021 Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang, Bo Chen, Mingyuan Zhou

As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.

Language Modelling

Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network

1 code implementation30 Jun 2021 Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou

However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers.

Topic Models Variational Inference

Probabilistic Margins for Instance Reweighting in Adversarial Training

no code implementations15 Jun 2021 Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.

Bayesian Attention Belief Networks

no code implementations9 Jun 2021 Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou

Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks.

Machine Translation Question Answering +2

Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning

no code implementations10 May 2021 Dandan Guo, Ruiying Lu, Bo Chen, Zequn Zeng, Mingyuan Zhou

Observing a set of images and their corresponding paragraph-captions, a challenging task is to learn how to produce a semantically coherent paragraph to describe the visual content of an image.

Image Paragraph Captioning Language Modelling +1

Contrastive Attraction and Contrastive Repulsion for Representation Learning

no code implementations8 May 2021 Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, Mingyuan Zhou

Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights, demonstrating the efficacy of the proposed doubly contrastive strategy.

Contrastive Learning Representation Learning

Partition-Guided GANs

1 code implementation CVPR 2021 Mohammadreza Armandpour, Ali Sadeghian, Chunyuan Li, Mingyuan Zhou

We formulate two desired criteria for the space partitioner that aid the training of our mixture of generators: 1) to produce connected partitions and 2) provide a proxy of distance between partitions and data samples, along with a direction for reducing that distance.

Demystifying Assumptions in Learning to Discover Novel Classes

no code implementations8 Feb 2021 Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

In learning to discover novel classes (L2DNC), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes.

Meta-Learning

Topic-aware Contextualized Transformers

no code implementations1 Jan 2021 Ruiying Lu, Bo Chen, Dan dan Guo, Dongsheng Wang, Mingyuan Zhou

Moving beyond conventional Transformers that ignore longer-range word dependencies and contextualize their word representations at the segment level, the proposed method not only captures global semantic coherence of all segments and global word concurrence patterns, but also enriches the representation of each token by adapting it to its local context, which is not limited to the segment it resides in and can be flexibly defined according to the task.

Word Embeddings

Polarimetric Helmholtz Stereopsis

no code implementations ICCV 2021 Yuqi Ding, Yu Ji, Mingyuan Zhou, Sing Bing Kang, Jinwei Ye

Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i. e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance.

3D Reconstruction

Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions

no code implementations28 Dec 2020 Huangjie Zheng, Mingyuan Zhou

The forward CT is the expected cost of moving a source data point to a target one, with their joint distribution defined by the product of the source probability density function (PDF) and a source-dependent conditional distribution, which is related to the target PDF via Bayes' theorem.

Bidirectional Convolutional Poisson Gamma Dynamical Systems

1 code implementation NeurIPS 2020 Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou

Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.

Bayesian Inference Document-level +1

Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network

no code implementations NeurIPS 2020 Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou

To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.

Topic Models

Convex Polytope Trees

1 code implementation21 Oct 2020 Mohammadreza Armandpour, Mingyuan Zhou

The splitting function at each node of CPT is based on the logical disjunction of a community of differently weighted probabilistic linear decision-makers, which also geometrically corresponds to a convex polytope in the covariate space.

Bayesian Attention Modules

1 code implementation NeurIPS 2020 Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou

Attention modules, as simple and effective tools, have not only enabled deep neural networks to achieve state-of-the-art results in many domains, but also enhanced their interpretability.

Image Captioning Machine Translation +4

MCMC-Interactive Variational Inference

no code implementations2 Oct 2020 Quan Zhang, Huangjie Zheng, Mingyuan Zhou

Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also facilitate the design of MCMC transitions.

Variational Inference

Variational Temporal Deep Generative Model for Radar HRRP Target Recognition

no code implementations28 Sep 2020 Dandan Guo, Bo Chen, Wenchao Chen, Chaojie Wang, Hongwei Liu, Mingyuan Zhou

We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP.

Variational Inference

Graph Gamma Process Generalized Linear Dynamical Systems

1 code implementation25 Jul 2020 Rahi Kalantari, Mingyuan Zhou

We use the generated random graph, whose number of nonzero-degree nodes is finite, to define both the sparsity pattern and dimension of the latent state transition matrix of a (generalized) linear dynamical system.

Time Series

Implicit Distributional Reinforcement Learning

2 code implementations NeurIPS 2020 Yuguang Yue, Zhendong Wang, Mingyuan Zhou

To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution.

Distributional Reinforcement Learning OpenAI Gym

Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference

no code implementations15 Jun 2020 Hao Zhang, Bo Chen, Yulai Cong, Dandan Guo, Hongwei Liu, Mingyuan Zhou

Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model.

Bayesian Inference

Probabilistic Best Subset Selection via Gradient-Based Optimization

1 code implementation11 Jun 2020 Mingzhang Yin, Nhat Ho, Bowei Yan, Xiaoning Qian, Mingyuan Zhou

In high-dimensional statistics, variable selection is an optimization problem aiming to recover the latent sparse pattern from all possible covariate combinations.

Methodology

Bayesian Graph Neural Networks with Adaptive Connection Sampling

1 code implementation ICML 2020 Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian

We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.

Node Classification

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

no code implementations21 May 2020 Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou, Xiaoning Qian

Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.

Information Retrieval

Mutual Information Gradient Estimation for Representation Learning

1 code implementation ICLR 2020 Liangjian Wen, Yiji Zhou, Lirong He, Mingyuan Zhou, Zenglin Xu

To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions.

Representation Learning

Learnable Bernoulli Dropout for Bayesian Deep Learning

no code implementations12 Feb 2020 Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou, Xiaoning Qian

In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters.

Collaborative Filtering Image Classification +1

Discrete Action On-Policy Learning with Action-Value Critic

1 code implementation10 Feb 2020 Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou

Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension, making it challenging to apply existing on-policy gradient based deep RL algorithms efficiently.

OpenAI Gym

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

1 code implementation ICLR 2020 Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou

To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.

Image Captioning Program Synthesis

Recurrent Hierarchical Topic-Guided RNN for Language Generation

1 code implementation ICML 2020 Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou

To simultaneously capture syntax and global semantics from a text corpus, we propose a new larger-context recurrent neural network (RNN) based language model, which extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation.

Language Modelling Text Generation

Meta-Learning without Memorization

1 code implementation ICLR 2020 Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn

If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.

Few-Shot Image Classification Meta-Learning

Weibull Racing Survival Analysis for Competing Events and a Study of Loan Payoff and Default

no code implementations2 Nov 2019 Quan Zhang, Qiang Gao, Mingfeng Lin, Mingyuan Zhou

We propose Bayesian nonparametric Weibull delegate racing (WDR) to explicitly model surviving under competing events and to interpret how the covariates accelerate or decelerate the event times.

Survival Analysis Methodology

ARSM Gradient Estimator for Supervised Learning to Rank

no code implementations1 Nov 2019 Siamak Zamani Dadaneh, Shahin Boluki, Mingyuan Zhou, Xiaoning Qian

Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches.

Learning-To-Rank

Thompson Sampling via Local Uncertainty

1 code implementation ICML 2020 Zhendong Wang, Mingyuan Zhou

Variational inference is used to approximate the posterior of the local variable, and semi-implicit structure is further introduced to enhance its expressiveness.

Decision Making Multi-Armed Bandits +1

Poisson-Randomized Gamma Dynamical Systems

1 code implementation NeurIPS 2019 Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna Wallach

This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness.

Semi-Implicit Stochastic Recurrent Neural Networks

no code implementations28 Oct 2019 Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian

Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.

Variational Inference

Variational Graph Recurrent Neural Networks

1 code implementation NeurIPS 2019 Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian

Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.

Dynamic Link Prediction Representation Learning +1

Semi-Implicit Graph Variational Auto-Encoders

1 code implementation NeurIPS 2019 Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian

Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.

Variational Inference

Semi-Implicit Generative Model

no code implementations29 May 2019 Mingzhang Yin, Mingyuan Zhou

To combine explicit and implicit generative models, we introduce semi-implicit generator (SIG) as a flexible hierarchical model that can be trained in the maximum likelihood framework.

Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling

1 code implementation ICLR 2020 Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.

Convolutional Poisson Gamma Belief Network

1 code implementation14 May 2019 Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou

For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector.

Latent Variable Models

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables

1 code implementation4 May 2019 Mingzhang Yin, Yuguang Yue, Mingyuan Zhou

To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance.

Variational Autoencoders for Sparse and Overdispersed Discrete Data

1 code implementation2 May 2019 He Zhao, Piyush Rai, Lan Du, Wray Buntine, Mingyuan Zhou

Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data.

Collaborative Filtering Multi-Label Learning +1

VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning

no code implementations ICLR 2019 Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou

To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network.

Image Generation Text Generation +2

Deep Topic Models for Multi-label Learning

no code implementations13 Apr 2019 Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai

We present a probabilistic framework for multi-label learning based on a deep generative model for the binary label vector associated with each observation.

Multi-Label Learning Topic Models

Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field

no code implementations9 Apr 2019 Mingyuan Zhou, Yu Ji, Yuqi Ding, Jinwei Ye, S. Susan Young, Jingyi Yu

In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the shape and reflectance of surfaces with arbitrary material in one shot.

Depth Estimation

3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights

no code implementations4 Apr 2019 Zhang Chen, Yu Ji, Mingyuan Zhou, Sing Bing Kang, Jingyi Yu

We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile.

3D Face Reconstruction Semantic Segmentation

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

no code implementations13 Mar 2019 Yunhao Tang, Mingzhang Yin, Mingyuan Zhou

Due to the high variance of policy gradients, on-policy optimization algorithms are plagued with low sample efficiency.

Dirichlet belief networks for topic structure learning

2 code implementations NeurIPS 2018 He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures.

Topic Models

Deep Poisson gamma dynamical systems

no code implementations NeurIPS 2018 Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies.

Data Augmentation Time Series

Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

no code implementations NeurIPS 2018 Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian

Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity.

Multi-Task Learning

Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

1 code implementation NeurIPS 2018 Quan Zhang, Mingyuan Zhou

We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event.

Data Augmentation Survival Analysis

ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks

1 code implementation ICLR 2019 Mingzhang Yin, Mingyuan Zhou

To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computational complexity.

Data Augmentation Latent Variable Models +1

Semi-Implicit Variational Inference

1 code implementation ICML 2018 Mingzhang Yin, Mingyuan Zhou

Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution.

Bayesian Inference Variational Inference

Parsimonious Bayesian deep networks

2 code implementations NeurIPS 2018 Mingyuan Zhou

Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model.

Model Selection

Masking: A New Perspective of Noisy Supervision

2 code implementations NeurIPS 2018 Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya zhang, Masashi Sugiyama

It is important to learn various types of classifiers given training data with noisy labels.

Ranked #27 on Image Classification on Clothing1M (using extra training data)

Image Classification

Locally Private Bayesian Inference for Count Models

1 code implementation22 Mar 2018 Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach

We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the social sciences.

Bayesian Inference Link Prediction

Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain

no code implementations7 Mar 2018 Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Paul de Figueiredo, Sing-Hoi Sze, Mingyuan Zhou, Xiaoning Qian

Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments.

Data Augmentation

WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling

1 code implementation ICLR 2018 Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and autoencoding variational Bayes.

Nonparametric Bayesian Sparse Graph Linear Dynamical Systems

no code implementations21 Feb 2018 Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou

A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data.

Time Series

Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC

no code implementations ICML 2017 Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou

It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers.

Data Augmentation Latent Variable Models

Poisson--Gamma Dynamical Systems

1 code implementation19 Jan 2017 Aaron Schein, Mingyuan Zhou, Hanna Wallach

We introduce a new dynamical system for sequentially observed multivariate count data.

Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

no code implementations30 Dec 2016 Quan Zhang, Mingyuan Zhou

To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks.

Poisson-Gamma dynamical systems

1 code implementation NeurIPS 2016 Aaron Schein, Hanna Wallach, Mingyuan Zhou

This paper presents a dynamical system based on the Poisson-Gamma construction for sequentially observed multivariate count data.

Softplus Regressions and Convex Polytopes

no code implementations23 Aug 2016 Mingyuan Zhou

To construct flexible nonlinear predictive distributions, the paper introduces a family of softplus function based regression models that convolve, stack, or combine both operations by convolving countably infinite stacked gamma distributions, whose scales depend on the covariates.

Bayesian Inference Data Augmentation

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

1 code implementation6 Jun 2016 Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna Wallach

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data.

Rotational Crossed-Slit Light Field

no code implementations CVPR 2016 Nianyi Li, Haiting Lin, Bilin Sun, Mingyuan Zhou, Jingyi Yu

In this paper, we present a novel LF sampling scheme by exploiting a special non-centric camera called the crossed-slit or XSlit camera.

Stereo Matching Stereo Matching Hand

Nonparametric Bayesian Negative Binomial Factor Analysis

no code implementations25 Apr 2016 Mingyuan Zhou

A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood.

Latent Variable Models

Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices

no code implementations30 Dec 2015 Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou

A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors.

Data Augmentation

Gamma Belief Networks

no code implementations9 Dec 2015 Mingyuan Zhou, Yulai Cong, Bo Chen

To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its hidden layers into the product of a sparse connection weight matrix and the nonnegative real hidden units of the next layer.

The Poisson Gamma Belief Network

no code implementations NeurIPS 2015 Mingyuan Zhou, Yulai Cong, Bo Chen

Example results on text analysis illustrate interesting relationships between the width of the first layer and the inferred network structure, and demonstrate that the PGBN, whose hidden units are imposed with correlated gamma priors, can add more layers to increase its performance gains over Poisson factor analysis, given the same limit on the width of the first layer.

Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

no code implementations25 Jan 2015 Mingyuan Zhou

A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link.

Community Detection Data Augmentation +1

Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling

no code implementations NeurIPS 2014 Mingyuan Zhou

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix.

Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling

no code implementations28 Oct 2014 Mingyuan Zhou

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix.

Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes

no code implementations12 Apr 2014 Mingyuan Zhou, Oscar Hernan Madrid Padilla, James G. Scott

We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns.

Feature Selection Text Classification

Generalized Negative Binomial Processes and the Representation of Cluster Structures

no code implementations7 Oct 2013 Mingyuan Zhou

The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions.

Augment-and-Conquer Negative Binomial Processes

no code implementations NeurIPS 2012 Mingyuan Zhou, Lawrence Carin

By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework.

Data Augmentation

Negative Binomial Process Count and Mixture Modeling

1 code implementation15 Sep 2012 Mingyuan Zhou, Lawrence Carin

A gamma process is employed to model the rate measure of a Poisson process, whose normalization provides a random probability measure for mixture modeling and whose marginalization leads to an NB process for count modeling.

Bayesian Inference

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