  Deep neural networks(NNs) have achieved impressive performance, often exceed
human performance on many computer vision tasks. However, one of the most
challenging issues that still remains is that NNs are overconfident in their
predictions, which can be very harmful when this arises in safety critical
applications. In this paper, we show that predictive uncertainty can be
efficiently estimated when we incorporate the concept of gradients uncertainty
into posterior sampling. The proposed method is tested on two different
datasets, MNIST for in-distribution confusing examples and notMNIST for
out-of-distribution data. We show that our method is able to efficiently
represent predictive uncertainty on both datasets.
