224 citations · 3.2k across the 170 of their papers we have counts for
15 papers · 1 filter
A Communication-Efficient Parallel Method for Group-Lasso
Binghong Chen, Jun Zhu
Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables. However, most existing…
Collaborative Filtering with User-Item Co-Autoregressive Models
Chao Du, Chongxuan Li, Yin Zheng +2
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the unde…
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data
Jingkang Yang, Haohan Wang, Jun Zhu +1
Understanding how brain functions has been an intriguing topic for years. With the recent progress on collecting massive data and developing advanced technology, people have become…
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Chongxuan Li, Jun Zhu, Bo Zhang
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. How…
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs
Kaiwei Li, Jianfei Chen, Wenguang Chen +1
Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images. Applications require LDA to handle both large datasets and a large nu…
Kernel Bayesian Inference with Posterior Regularization
Yang Song, Jun Zhu, Yong Ren
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equ…
Conditional Generative Moment-Matching Networks
Yong Ren, Jialian Li, Yucen Luo +1
Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this…
PSDVec: a Toolbox for Incremental and Scalable Word Embedding
Shaohua Li, Jun Zhu, Chunyan Miao
PSDVec is a Python/Perl toolbox that learns word embeddings, i.e. the mapping of words in a natural language to continuous vectors which encode the semantic/syntactic regularities…
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Shaohua Li, Tat-Seng Chua, Jun Zhu +1
Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic mo…
Towards Better Analysis of Deep Convolutional Neural Networks
Mengchen Liu, Jiaxin Shi, Zhen Li +3
Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-qu…
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Jun Zhu, Jiaming Song, Bei Chen
Link prediction is a fundamental task in statistical network analysis. Recent advances have been made on learning flexible nonparametric Bayesian latent feature models for link pre…
Scaling up Dynamic Topic Models
Arnab Bhadury, Jianfei Chen, Jun Zhu +1
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods a…
Spectral Learning for Supervised Topic Models
Yong Ren, Yining Wang, Jun Zhu
Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference…
Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang
Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exist…
Streaming Gibbs Sampling for LDA Model
Yang Gao, Jianfei Chen, Jun Zhu
Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have litt…