Dynamics of the adaptive natural gradient descent method for soft committee machines

Masato Inoue*, Hyeyoung Park, Masato Okada

*この研究の対応する著者

研究成果: Article査読

4 被引用数 (Scopus)

抄録

The learning efficiency of a simplified version of adaptive natural gradient descent (ANGD) for soft committee machines was evaluated. Statistical-mechanical techniques, which extract order parameters and make the stochastic learning dynamics converge towards deterministic at the large limit of the input dimension N [1,2], were employed. ANGD was found to perform as well as natural gradient descent (NGD). The key condition affecting the learning plateau in ANGD were also revealed.

本文言語English
論文番号056120
ページ(範囲)056120-1-056120-14
ジャーナルPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
69
5 1
出版ステータスPublished - 2004 5 1
外部発表はい

ASJC Scopus subject areas

  • 統計物理学および非線形物理学
  • 統計学および確率
  • 凝縮系物理学

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