  Bayesian Neural Networks (BNNs) have recently received increasing attention
for their ability to provide well-calibrated posterior uncertainties. However,
model selection---even choosing the number of nodes---remains an open question.
Recent work has proposed the use of a horseshoe prior over node pre-activations
of a Bayesian neural network, which effectively turns off nodes that do not
help explain the data. In this work, we propose several modeling and inference
advances that consistently improve the compactness of the model learned while
maintaining predictive performance, especially in smaller-sample settings
including reinforcement learning.
