Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights

16 Nov 2018Melanie F. PradierWeiwei PanJiayu YaoSoumya GhoshFinale Doshi-velez

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... (read more)

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