Computing with infinite networks

Christopher K. I. Williams

    Research output: Chapter in Book/Report/Conference proceedingConference publication

    Abstract

    For neural networks with a wide class of weight-priors, it can be shown that in the limit of an infinite number of hidden units the prior over functions tends to a Gaussian process. In this paper analytic forms are derived for the covariance function of the Gaussian processes corresponding to networks with sigmoidal and Gaussian hidden units. This allows predictions to be made efficiently using networks with an infinite number of hidden units, and shows that, somewhat paradoxically, it may be easier to compute with infinite networks than finite ones.
    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems
    EditorsM. C. Mozer, M. I. Jordan, T. Petsche
    Place of PublicationCambridge, US
    PublisherMIT
    Pages265-301
    Number of pages37
    ISBN (Print)0262100657
    Publication statusPublished - 1996
    Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United Kingdom
    Duration: 2 Dec 19965 Dec 1996

    Publication series

    NameProceesing of the 1996 conference
    PublisherMassachusetts Institute of Technology Press (MIT Press)

    Conference

    Conference10th Annual Conference on Neural Information Processing Systems, NIPS 1996
    CountryUnited Kingdom
    CityDenver, CO
    Period2/12/965/12/96

    Bibliographical note

    Copyright of the Massachusetts Institute of Technology Press (MIT Press)

    Keywords

    • neural networks
    • weight-priors
    • Gaussian process
    • sigmoidal

    Fingerprint Dive into the research topics of 'Computing with infinite networks'. Together they form a unique fingerprint.

    Cite this