Bayesian Inferring Networks

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Machine Learning

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Bayesian Inferring Networks

A Bayesian Approach to Invariant Deep Neural Networks

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 specificinvariances.

Authors

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

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