Sampling hidden parameters from oracle distribution

Sho Sonoda, Noboru Murata

    研究成果: Conference contribution

    3 引用 (Scopus)

    抜粋

    A new sampling learning method for neural networks is proposed. Derived from an integral representation of neural networks, an oracle probability distribution of hidden parameters is introduced. In general rigorous sampling from the oracle distribution holds numerical difficulty, a linear-time sampling algorithm is also developed. Numerical experiments showed that when hidden parameters were initialized by the oracle distribution, following backpropagation converged faster to better parameters than when parameters were initialized by a normal distribution.

    元の言語English
    ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    出版者Springer Verlag
    ページ539-546
    ページ数8
    8681 LNCS
    ISBN(印刷物)9783319111780
    DOI
    出版物ステータスPublished - 2014
    イベント24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
    継続期間: 2014 9 152014 9 19

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    8681 LNCS
    ISSN(印刷物)03029743
    ISSN(電子版)16113349

    Other

    Other24th International Conference on Artificial Neural Networks, ICANN 2014
    Germany
    Hamburg
    期間14/9/1514/9/19

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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  • これを引用

    Sonoda, S., & Murata, N. (2014). Sampling hidden parameters from oracle distribution. : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (巻 8681 LNCS, pp. 539-546). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 8681 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_68