  Calibrating the confidence of supervised learning models is important for a
variety of contexts where the certainty over predictions should be reliable.
However, it has been reported that deep neural network models are often too
poorly calibrated for achieving complex tasks requiring reliable uncertainty
estimates in their prediction. In this work, we are proposing a strategy for
training deep ensembles with a diversity function regularization, which
improves the calibration property while maintaining a similar prediction
accuracy.
