  We propose a novel Bayesian neural network architecture that can learn
invariances from data alone by inferring a posterior distribution over
different weight-sharing schemes. We show that our model outperforms other
non-invariant architectures, when trained on datasets that contain specific
invariances. The same holds true when no data augmentation is performed.
