uncertainty in deep learning

深度贝叶斯学习: In this work we develop tools to obtain practical uncertainty estimates in deep learning, casting recent deep learning tools as Bayesian models without changing either the models or the optimisation. In the first part of this thesis we develop the theory for such tools, providing applications and illustrative examples. We tie approximate inference in Bayesian models to dropout and other stochastic regularisation techniques, and assess the approximations empirically. We give example applications arising from this connection between modern deep learning and Bayesian modelling such as active learning of image data and data-efficient deep reinforcement learning. We further demonstrate the tools’ practicality through a survey of recent applications making use of the suggested techniques in language applications, medical diagnostics, bioinformatics, image processing, and autonomous driving. In the second part of the thesis we explore the insights stemming from the link between Bayesian modelling and deep learning, and its theoretical implications. We discuss what determines model uncertainty properties, analyse the approximate inference analytically in the linear case, and theoretically examine various priors such as spike and slab priors.