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Found 3 papers, 2 papers with code
Multivariate Bayesian Last Layer for Regression: Uncertainty Quantification and Disentanglement
We present new Bayesian Last Layer models in the setting of multivariate regression under heteroscedastic noise, and propose an optimization algorithm for parameter learning.
Data Subsampling for Bayesian Neural Networks
Since it requires the computation of a so-called "noise penalty" determined by the variance of the training loss function over the mini-batches, we refer to this data subsampling strategy as Penalty Bayesian Neural Networks - PBNNs.
Discretely Indexed Flows
In this paper we propose Discretely Indexed flows (DIF) as a new tool for solving variational estimation problems.
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