11 citations · 43 across the 15 of their papers we have counts for
15 papers
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
Rui Zhang, Qi Meng, Rongchan Zhu +5
In scenarios with limited available data, training the function-to-function neural PDE solver in an unsupervised manner is essential. However, the efficiency and accuracy of existi…
Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime
Rui Zhang, Shihua Zhang
Understanding the black-box prediction for neural networks is challenging. To achieve this, early studies have designed influence function (IF) to measure the effect of removing a…
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
Chihao Zhang, Yiling Elaine Chen, Shihua Zhang +1
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitioners commonly combine ambiguous outcome labels for all data points (instances) in…
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…
Deep Residual Networks Learn the Geodesic Curve in the Wasserstein Space
Kuo Gai, Shihua Zhang
Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNNs, especially deep residual netw…
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…
Sparse Weighted Canonical Correlation Analysis
Wenwen Min, Juan Liu, Shihua Zhang
Given two data matrices and , sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors and to maximize the correlation between and $…
Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
Wenwen Min, Juan Liu, Shihua Zhang
Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. However, it is necessary to integrate other prior knowledge lik…
L0-norm Sparse Graph-regularized SVD for Biclustering
Wenwen Min, Juan Liu, Shihua Zhang
Learning the "blocking" structure is a central challenge for high dimensional data (e.g., gene expression data). Recently, a sparse singular value decomposition (SVD) has been used…
MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification
Wei Vivian Li, Anqi Zhao, Shihua Zhang +1
Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into ge…
A neurodynamic framework for local community extraction in networks
Shihua Zhang, Guanghua Hu, Wenwen Min
To understand the structure and organization of a large-scale social, biological or technological network, it can be helpful to describe and extract local communities or modules of…