The Epistemic Uncertainty Hole: an issue of Bayesian Neural Networks

More precisely, we observe that the epistemic uncertainty In many applications of Machine Learning, optimizing collapses literally in the presence of large models and solely the performance metrics of the predictive model, sometimes also of little training data, while we expect the such as the accuracy, can result in overconfident interpretations exact opposite behaviour. This phenomenon, which we call of erroneous outcomes, and thus, hazardous decisions "epistemic uncertainty hole", is all the more problematic as in case of critical domains. Therefore, being able to map the it undermines the entire applicative potential of BDL, which model outputs to some uncertainty quantification metrics, if is based precisely on the use of epistemic uncertainty. As well calibrated, is essential from a decision making point of an example, we evaluate the practical consequences of this view. When dealing with Deep Learning models, Bayesian uncertainty hole on one of the main applications of BDL, Deep Learning (BDL) [11, 12, 18, 10, 2], i.e. the application namely the detection of out-of-distribution samples. of Bayesian inference to deep neural networks, appears to be one of the keys to estimate such well-calibrated uncertainties.