Implementing size-optimal discrete neural networks requires analog circuitry

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Neural networks (NNs) have been experimentally shown to be quite effective in many applications. This success has led researchers to undertake a rigorous analysis of the mathematical properties that enable them to perform so well. It has generated two directions of research: (i) to find existence/constructive proofs for what is now known as the universal approximation problem; (ii) to find tight bounds on the size needed by the approximation problem (or some particular cases). The paper will focus on both aspects, for the particular case when the functions to be implemented are Boolean.

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6 p.

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Beiu, V. March 1, 1998.

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Description

Neural networks (NNs) have been experimentally shown to be quite effective in many applications. This success has led researchers to undertake a rigorous analysis of the mathematical properties that enable them to perform so well. It has generated two directions of research: (i) to find existence/constructive proofs for what is now known as the universal approximation problem; (ii) to find tight bounds on the size needed by the approximation problem (or some particular cases). The paper will focus on both aspects, for the particular case when the functions to be implemented are Boolean.

Physical Description

6 p.

Notes

OSTI as DE98005765

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  • 9. European signal processing conference, Rhodes (Greece), 8-11 Sep 1998

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  • Other: DE98005765
  • Report No.: LA-UR--97-4432
  • Report No.: CONF-980911--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 658441
  • Archival Resource Key: ark:/67531/metadc712302

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  • March 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • May 5, 2016, 7:32 p.m.

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Beiu, V. Implementing size-optimal discrete neural networks requires analog circuitry, article, March 1, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc712302/: accessed September 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.