  Stochastic variational inference for Bayesian deep neural network (DNN)
requires specifying priors and approximate posterior distributions over neural
network weights. Specifying meaningful weight priors is a challenging problem,
particularly for scaling variational inference to deeper architectures
involving high dimensional weight space. We propose MOdel Priors with Empirical
Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian
neural networks. We formulate a two-stage hierarchical modeling, first find the
maximum likelihood estimates of weights with DNN, and then set the weight
priors using empirical Bayes approach to infer the posterior with variational
inference. We empirically evaluate the proposed approach on real-world tasks
including image classification, video activity recognition and audio
classification with varying complex neural network architectures. We also
evaluate our proposed approach on diabetic retinopathy diagnosis task and
benchmark with the state-of-the-art Bayesian deep learning techniques. We
demonstrate MOPED method enables scalable variational inference and provides
reliable uncertainty quantification.
