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Transients and Asymptotics of Natural Gradient Learning

  • Magnus Rattray
  • David Saad
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical mechanics framework which is appropriate for large input dimension. We find significant improvement over standard gradient descent in both the transient and asymptotic phases of learning.

Keywords

Learning Rate Gradient Descent Fisher Information Fisher Information Matrix Generalization Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Magnus Rattray
    • 1
  • David Saad
    • 1
  1. 1.Neural Computing Research GroupAston UniversityBirminghamUK

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