bayesian-neural-networks

This paper studies data augmentation for Bayesian neural networks trained with variational inference, deriving conditions for exact equivariance and introducing novel symmetrization techniques like orbit expansion to improve symmetry and performance.

Proposes EVIDENT, a framework that integrates Bayesian training and evidence-based ranking for neural architecture selection, demonstrated on subject-specific blood glucose forecasting in type 1 diabetes, systematically selecting low-capacity models that generalize reliably.