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Found 5 papers, 5 papers with code
Generalizing Variational Autoencoders with Hierarchical Empirical Bayes
Recent methods have mitigated this issue by deterministically moment-matching an aggregated posterior distribution to an aggregate prior.
Interpreting Deep Neural Networks Through Variable Importance
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited.
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression.
Functional Data Analysis using a Topological Summary Statistic: the Smooth Euler Characteristic Transform
We introduce a novel statistic, the smooth Euler characteristic transform (SECT), which is designed to integrate shape information into regression models by representing shapes and surfaces as a collection of curves.
Applications
Bayesian Approximate Kernel Regression with Variable Selection
State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively.
