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Optimising the topology of complex neural networks

Fei Jiang 1, 2 Hugues Berry 2 Marc Schoenauer 1
1 TANC - Algorithmic number theory for cryptology
Inria Saclay - Ile de France, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
2 ALCHEMY - Architectures, Languages and Compilers to Harness the End of Moore Years
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France
Abstract : In this paper, we study instances of complex neural networks, i.e. neural netwo rks with complex topologies. We use Self-Organizing Map neural networks whose n eighbourhood relationships are defined by a complex network, to classify handwr itten digits. We show that topology has a small impact on performance and robus tness to neuron failures, at least at long learning times. Performance may howe ver be increased (by almost $10\%$) by artificial evolution of the network topo logy. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution.
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https://hal.inria.fr/inria-00175721
Contributor : Marc Schoenauer <>
Submitted on : Monday, October 1, 2007 - 8:45:14 AM
Last modification on : Wednesday, September 16, 2020 - 5:06:42 PM
Long-term archiving on: : Friday, April 9, 2010 - 4:47:03 PM

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  • HAL Id : inria-00175721, version 1
  • ARXIV : 0710.0213

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Fei Jiang, Hugues Berry, Marc Schoenauer. Optimising the topology of complex neural networks. ECCS'07, Complex Systesms Society, Oct 2007, Dresden, Germany. ⟨inria-00175721⟩

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