  Standard deep learning methods, such as Ensemble Models, Bayesian Neural
Networks and Quantile Regression Models provide estimates to prediction
uncertainties for data-driven deep learning models. However, they can be
limited in their applications due to their heavy memory, inference cost, and
ability to properly capture out-of-distribution uncertainties. Additionally,
some of these models require post-training calibration which limits their
ability to be used for continuous learning applications. In this paper, we
present a new approach to provide prediction with calibrated uncertainties that
includes out-of-distribution contributions and compare it to standard methods
on the Fermi National Accelerator Laboratory (FNAL) Booster accelerator
complex.
