1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2026★ 1 cited
A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
David J. Schodt, Ryan Brown, Michael Merritt +3
Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications. Often, heteroscedastic aleatoric uncertainties are learned as…
cs.LG2024
Few-sample Variational Inference of Bayesian Neural Networks with Arbitrary Nonlinearities
David J. Schodt
Bayesian Neural Networks (BNNs) extend traditional neural networks to provide uncertainties associated with their outputs. On the forward pass through a BNN, predictions (and their…