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- W2896860804 abstract "Understanding the uncertainty of a neural network's (NN) predictions isessential for many applications. The Bayesian framework provides a principledapproach to this, however applying it to NNs is challenging due to the largenumber of parameters and data. Ensembling NNs provides a practical and scalablemethod for uncertainty quantification. Its drawback is that its justificationis heuristic rather than Bayesian. In this work we propose one modification tothe usual ensembling process, that does result in Bayesian behaviour:regularising parameters about values drawn from a prior distribution. Hence, wepresent an easily implementable, scalable technique for performing approximateBayesian inference in NNs." @default.
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- W2896860804 date "2018-10-12" @default.
- W2896860804 modified "2023-10-04" @default.
- W2896860804 title "Uncertainty in Neural Networks: Bayesian Ensembling." @default.
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