FiLM-Ensemble: ProbabilisticDeepLearningvia Feature-wiseLinearModulation

Acommon approach toquantify epistemic uncertainty, usable across a wide class of prediction models, is to train amodel ensemble. In a naïve implementation, the ensemble approach has high computational cost and high memory demand. This challenges in particular modern deep learning, where evenasingle deep network isalready demanding interms ofcompute and memory,and has givenrise toanumber ofattempts toemulate the model ensemble without actually instantiating separate ensemble members.