Finite-size effects in on-line learning of multilayer neural networks

David Barber, David Saad, Peter Sollich

Research output: Contribution to journalArticlepeer-review


We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learning dynamics in multi-layer networks by calculating fluctuations possessed by finite dimensional systems. Fluctuations from the mean dynamics are largest at the onset of specialisation as student hidden unit weight vectors begin to imitate specific teacher vectors, increasing with the degree of symmetry of the initial conditions. In light of this, we include a term to stimulate asymmetry in the learning process, which typically also leads to a significant decrease in training time.
Original languageEnglish
Pages (from-to)151-156
Number of pages6
JournalEurophysics Letters
Issue number2
Publication statusPublished - Apr 1996

Bibliographical note

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  • probability theory
  • stochastic processes
  • and statistics


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