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.

翻译：本文提出了一个概率框架,以获得对深神经网络预测的可靠和快速的不确定性估计。我们的主要贡献是将DNN与稀有高斯进程(GP)进行实际和原则的结合。我们理论上证明DNN可以被视为稀有的GP(即GP专家混合物)的特殊案例,我们设计了一种学习算法,将衍生理论付诸实践。在两种不同机器人任务 -- -- 操纵器和微型飞行器物体探测的反动动态 -- -- 的实验中,我们展示了我们的方法在预测不确定性、可缩放性提高和杰特森TX2的运行时效率方面的有效性。 我们因此认为,我们的方法可以为可靠和快速的机器人学习系统铺平道路,并具有不确定性意识。