A Bayesian Approach to Invariant Deep Neural Networks

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos +2

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 s…