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Sparsity and Complexity of Networks Computing Highly-Varying Functions

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00493825" target="_blank" >RIV/67985807:_____/18:00493825 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-030-01424-7_52" target="_blank" >http://dx.doi.org/10.1007/978-3-030-01424-7_52</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-01424-7_52" target="_blank" >10.1007/978-3-030-01424-7_52</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Sparsity and Complexity of Networks Computing Highly-Varying Functions

  • Original language description

    Approximative measures of network sparsity in terms of norms tailored to dictionaries of computational units are investigated. Lower bounds on these norms of real-valued functions on finite domains are derived. The bounds are proven by combining the concentration of measure property of high-dimensional spaces with characterization of dictionaries of computational units in terms of their capacities and coherence measured by their covering numbers. The results are applied to dictionaries used in neurocomputing which have power-type covering numbers. Probabilistic results are illustrated by a concrete construction of a class of functions, computation of which by perceptron networks requires large number of units or it is unstable due to large output weights.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part III

  • ISBN

    978-3-030-01423-0

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    534-543

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Rhodes

  • Event date

    Oct 4, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article