Why deep neural networks: A possible theoretical explanation

Chitta Baral, Olac Fuentes, Vladik Kreinovich

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

In the past, the most widely used neural networks were 3-layer ones. These networks were preferred, since one of the main advantages of the biological neural networks—which motivated the use of neural networks in computing—is their parallelism, and 3-layer networks provide the largest degree of parallelism. Recently, however, it was empirically shown that, in spite of this argument, multi-layer (“deep”) neural networks leads to a much more efficient machine learning. In this paper, we provide a possible theoretical explanation for the somewhat surprising empirical success of deep networks.

Original languageEnglish (US)
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer International Publishing
Pages1-5
Number of pages5
DOIs
StatePublished - Jan 1 2018

Publication series

NameStudies in Systems, Decision and Control
Volume100
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

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ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)

Cite this

Baral, C., Fuentes, O., & Kreinovich, V. (2018). Why deep neural networks: A possible theoretical explanation. In Studies in Systems, Decision and Control (pp. 1-5). (Studies in Systems, Decision and Control; Vol. 100). Springer International Publishing. https://doi.org/10.1007/978-3-319-61753-4_1