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A Bayesian Approach to Invariant Deep Neural Networks
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark v · 2021-07-20 · via stat.ML updates on arXiv.org

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.

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