f8905bd3df64ace64a68e154ba72f24c-Paper.pdf

Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under-orover-confident intheir predictions. Consequently,methods have been developed to improve the calibration of their predictive uncertainty, both during training and post-hoc.