  This paper presents a probabilistic framework to obtain both reliable and
fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).
Our main contribution is a practical and principled combination of DNNs with
sparse Gaussian Processes (GPs). We prove theoretically that DNNs can be seen
as a special case of sparse GPs, namely mixtures of GP experts (MoE-GP), and we
devise a learning algorithm that brings the derived theory into practice. In
experiments from two different robotic tasks -- inverse dynamics of a
manipulator and object detection on a micro-aerial vehicle (MAV) -- we show the
effectiveness of our approach in terms of predictive uncertainty, improved
scalability, and run-time efficiency on a Jetson TX2. We thus argue that our
approach can pave the way towards reliable and fast robot learning systems with
uncertainty awareness.
