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Found 4 papers, 2 papers with code
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms.
Bayesian Semi-structured Subspace Inference
To this end, we extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects.
Bernstein Flows for Flexible Posteriors in Variational Bayes
Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization.
Transformation Models for Flexible Posteriors in Variational Bayes
In Bayesian neural networks, variational inference is widely used to approximate difficult-to-compute posteriors by variational distributions.
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