Training Recurrent Networks

Morten With Pedersen

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    Abstract

    Training recurrent networks is generally believed to be a difficult task. Excessive training times and lack of convergence to an acceptable solution are frequently reported. In this paper we seek to explain the reason for this from a numerical point of view and show how to avoid problems when training. In particular we investigate ill-conditioning, the need for and effect of regularization and illustrate the superiority of second-order methods for training
    Original languageEnglish
    Title of host publicationIEEE Workshop on Neural Networks for Signal Processing VII
    Place of PublicationPiscataway, New Jersey
    PublisherIEEE
    Publication date1997
    Pages355-364
    ISBN (Print)0-7803-4256-9
    DOIs
    Publication statusPublished - 1997
    Event1997 IEEE Workshop on Neural Networks for Signal Processing VII - Amelia Island, United States
    Duration: 24 Sept 199726 Sept 1997
    Conference number: 7
    https://ieeexplore.ieee.org/xpl/conhome/4900/proceeding

    Conference

    Conference1997 IEEE Workshop on Neural Networks for Signal Processing VII
    Number7
    Country/TerritoryUnited States
    CityAmelia Island
    Period24/09/199726/09/1997
    Internet address

    Bibliographical note

    Copyright: 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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