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Proceedings Paper

Analysis of feedforward networks
Author(s): Ronald K. Pearson
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Paper Abstract

Feedforward networks are used extensively in practice to learn static mappings between related sets of variables. These networks are difficult to analyze, however, both because of their nonlinearity and their complex interconnection structure. In the absence of the nonlinearity, linear algebra could provide considerable insight into the behavior of these networks, significantly beyond that possible from a detailed analysis of individual neurons. Such insights would be extremely valuable since the power of neural networks arises from their large-scale connectivity, rather than the inherent computational capacity of the individual neurons. This paper proposes algebraic category theory as the basis for obtaining such global insights for feedforward networks in spite of their nonlinearity.

Paper Details

Date Published: 16 December 1992
PDF: 11 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130827
Show Author Affiliations
Ronald K. Pearson, E.I. du Pont de Nemours & Co., Inc. (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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