# Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights

@article{Pradier2018LatentPB, title={Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights}, author={Melanie F. Pradier and Weiwei Pan and Jiayu Yao and Soumya Ghosh and Finale Doshi-Velez}, journal={ArXiv}, year={2018}, volume={abs/1811.07006} }

As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial. While modern neural networks are making remarkable gains in terms of predictive accuracy, characterizing uncertainty over the parameters of these models is challenging because of the high dimensionality and complex correlations of the network parameter space. This paper introduces a novel variational inference framework for Bayesian neural networks that (1) encodes… CONTINUE READING

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