Title: Combining model and parameter uncertainty in Bayesian neural networks
Authors: Geir Olve Storvik - University of Oslo (Norway) [presenting]
Aliaksandr Hubin - Norwegian Computing Center (Norway)
Abstract: Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a Bayesian approach: Parameter and prediction uncertainties become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated. However, so far, there have been no scalable techniques capable of combining both model (structural) and parameter uncertainty. We introduce the concept of model uncertainty in BNNs, and hence we make inference in the joint space of models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Finally, we show that incorporating model uncertainty via Bayesian model averaging and Bayesian model selection allows us to drastically sparsify the structure of BNNs.