Keeping the neural networks simple by minimizing the description length of the weights
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TL;DR
A method of computing the derivatives of the expected squared error and of the amount of information in the noisy weights in a network that contains a layer of non-linear hidden units without time-consuming Monte Carlo simulations is described.
Abstract
Article Keeping the neural networks simple by minimizing the description length of the weights Share on Authors: Geoffrey E. Hinton View Profile , Drew van Camp View Profile Authors Info & Claims COLT '93: Proceedings of the sixth annual conference on Computational learning theoryAugust 1993 Pages 5–13https://doi.org/10.1145/168304.168306Online:01 August 1993Publication History 235citation2,394DownloadsMetricsTotal Citations235Total Downloads2,394Last 12 Months367Last 6 weeks44 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access