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


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
Number of pages5
StatePublished - Jan 1 2018

Publication series

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


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