June 11, 2024, 4:46 a.m. | Nick Hauptvogel, Christian Igel

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05469v1 Announce Type: new
Abstract: Bayesian neural networks address epistemic uncertainty by learning a posterior distribution over model parameters. Sampling and weighting networks according to this posterior yields an ensemble model referred to as Bayes ensemble. Ensembles of neural networks (deep ensembles) can profit from the cancellation of errors effect: Errors by ensemble members may average out and the deep ensemble achieves better predictive performance than each individual network. We argue that neither the sampling nor the weighting in a …

abstract arxiv bayes bayesian cs.lg deep neural network distribution ensemble errors network networks neural network neural networks parameters posterior profit sampling type uncertainty

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