1 citations · 1 across the 3 of their papers we have counts for
8 papers
Adversarial Information Bottleneck
Penglong Zhai, Shihua Zhang
The information bottleneck (IB) principle has been adopted to explain deep learning in terms of information compression and prediction, which are balanced by a trade-off hyperparam…
Learnable Graph-regularization for Matrix Decomposition
Penglong Zhai, Shihua Zhang
Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields including computer vision, text mining, bioinformatics an…
Tessellated Wasserstein Auto-Encoders
Kuo Gai, Shihua Zhang
Non-adversarial generative models such as variational auto-encoder (VAE), Wasserstein auto-encoders with maximum mean discrepancy (WAE-MMD), sliced-Wasserstein auto-encoder (SWAE)…
Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Chihao Zhang, Yang Yang, Wei Zhang +1
Matrix decomposition is one of the fundamental tools to discover knowledge from big data generated by modern applications. However, it is still inefficient or infeasible to process…
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling
Chihao Zhang, Kuo Gai, Shihua Zhang
Principal component analysis (PCA) is one of the most widely used dimension reduction and multivariate statistical techniques. From a probabilistic perspective, PCA seeks a low-dim…
Group-sparse SVD Models and Their Applications in Biological Data
Wenwen Min, Juan Liu, Shihua Zhang
Sparse Singular Value Decomposition (SVD) models have been proposed for biclustering high dimensional gene expression data to identify block patterns with similar expressions. Howe…