  Ensembles of neural networks have been shown to give better performance than
single networks, both in terms of predictions and uncertainty estimation.
Additionally, ensembles allow the uncertainty to be decomposed into aleatoric
(data) and epistemic (model) components, giving a more complete picture of the
predictive uncertainty. Ensemble distillation is the process of compressing an
ensemble into a single model, often resulting in a leaner model that still
outperforms the individual ensemble members. Unfortunately, standard
distillation erases the natural uncertainty decomposition of the ensemble. We
present a general framework for distilling both regression and classification
ensembles in a way that preserves the decomposition. We demonstrate the desired
behaviour of our framework and show that its predictive performance is on par
with standard distillation.
