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Found 5 papers, 1 papers with code
Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework controlling FDR (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple-hypothesis testing.
Variational Interpretable Learning from Multi-view Data
We propose a deep interpretable variational canonical correlation analysis (DICCA) for multi-view learning.
NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy
Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science.
Interpretable Deep Representation Learning from Temporal Multi-view Data
In many scientific problems such as video surveillance, modern genomics, and finance, data are often collected from diverse measurements across time that exhibit time-dependent heterogeneous properties.
Probabilistic Canonical Correlation Analysis for Sparse Count Data
We further apply the PSCCA method to study the association of miRNA and mRNA expression data sets from a squamous cell lung cancer study, finding that PSCCA can uncover a large number of strongly correlated pairs than standard correlation and other sparse CCA approaches.
