190 citations · 1.1k across the 64 of their papers we have counts for
6 papers · 1 filter
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…
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…