63 citations · 243 across the 23 of their papers we have counts for
23 papers
Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
Chaojie Wang, Xinyang Liu, Dongsheng Wang +3
Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to appr…
A Non-negative VAE:the Generalized Gamma Belief Network
Zhibin Duan, Tiansheng Wen, Muyao Wang +2
The gamma belief network (GBN), often regarded as a deep topic model, has demonstrated its potential for uncovering multi-layer interpretable latent representations in text data. I…
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
Xinyang Liu, Dongsheng Wang, Bowei Fang +5
For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, whic…
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Yishi Xu, Dongsheng Wang, Bo Chen +3
Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, l…
Knowledge-Aware Bayesian Deep Topic Model
Dongsheng Wang, Yishi Xu, Miaoge Li +4
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Although embedded topic models (ETMs) and its variants have gained…
Ordinal Graph Gamma Belief Network for Social Recommender Systems
Dongsheng Wang, Chaojie Wang, Bo Chen +1
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships betwee…
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dandan Guo, He Zhao +4
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is…
A Variational Edge Partition Model for Supervised Graph Representation Learning
Yilin He, Chaojie Wang, Hao Zhang +2
Graph neural networks (GNNs), which propagate the node features through the edges and learn how to transform the aggregated features under label supervision, have achieved great su…
TopicNet: Semantic Graph-Guided Topic Discovery
Zhibin Duan, Yishi Xu, Bo Chen +3
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic…
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng +4
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampl…
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen +5
Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic…
Bayesian Attention Belief Networks
Shujian Zhang, Xinjie Fan, Bo Chen +1
Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less exp…
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning
Dandan Guo, Ruiying Lu, Bo Chen +2
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…
Bayesian Attention Modules
Xinjie Fan, Shujian Zhang, Bo Chen +1
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 interpreta…
Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
Dandan Guo, Bo Chen, Wenchao Chen +3
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 dep…
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Hao Zhang, Bo Chen, Yulai Cong +3
To build a flexible and interpretable model for document analysis, we develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its mul…
Recurrent Hierarchical Topic-Guided RNN for Language Generation
Dandan Guo, Bo Chen, Ruiying Lu +1
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 recurr…
Convolutional Poisson Gamma Belief Network
Chaojie Wang, Bo Chen, Sucheng Xiao +1
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. In this pape…
Deep Poisson gamma dynamical systems
Dandan Guo, Bo Chen, Hao Zhang +1
We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarc…
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
Hao Zhang, Bo Chen, Dandan Guo +1
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 aut…
Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions
Yulai Cong, Bo Chen, Mingyuan Zhou
We introduce a fast and easy-to-implement simulation algorithm for a multivariate normal distribution truncated on the intersection of a set of hyperplanes, and further generalize…
The Poisson Gamma Belief Network
Mingyuan Zhou, Yulai Cong, Bo Chen
To infer a multilayer representation of high-dimensional count vectors, we propose the Poisson gamma belief network (PGBN) that factorizes each of its layers into the product of a…
Gamma Belief Networks
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…