Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Deep learning tools have recently gained much attention in applied machine learning. However such tools for regression and classification do not allow us to capture model uncertainty. Bayesian models offer us the ability to reason about model uncertainty, but usually come with a prohibitive computational cost. We show that dropout in multilayer perceptron models (MLPs) can be interpreted as a Bayesian approximation. Results are obtained for modelling uncertainty for dropout MLP models - extracting information that has been thrown away so far, from existing models. This mitigates the problem of representing uncertainty in deep learning without sacrificing computational performance or test accuracy. We perform an exploratory study of the dropout uncertainty properties. Various network architectures and non-linearities are assessed on tasks of extrapolation, interpolation, and classification. We show that model uncertainty is important for classification tasks using MNIST as an example, and use the model's uncertainty in a Bayesian pipeline, with deep reinforcement learning as a concrete example.