About: Automatic Modularization of Artificial Neural Networks     Goto   Sponge   Distinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • The majority of this paper relies on some forms of automatic decomposition tasks into modules. Both described methods execute automatic neural network modularization. Modules in neural networks emerge; we do not build them straightforward by penalizing interference between modules. The concept of emergence takes an important role in the study of the design of neural networks. In the paper, we study an emergence of modular connectionist architecture of neural networks, in which networks composing the architecture compete to learn the training patterns directly from the interaction of reproduction with the task environment. Network architectures emerge from an initial set of randomly connected networks. In this way can be eliminated connections so as to dedicate different portions of the system to learn different tasks. Mentioned methods were demonstrated for experimental task solving.
  • The majority of this paper relies on some forms of automatic decomposition tasks into modules. Both described methods execute automatic neural network modularization. Modules in neural networks emerge; we do not build them straightforward by penalizing interference between modules. The concept of emergence takes an important role in the study of the design of neural networks. In the paper, we study an emergence of modular connectionist architecture of neural networks, in which networks composing the architecture compete to learn the training patterns directly from the interaction of reproduction with the task environment. Network architectures emerge from an initial set of randomly connected networks. In this way can be eliminated connections so as to dedicate different portions of the system to learn different tasks. Mentioned methods were demonstrated for experimental task solving. (en)
Title
  • Automatic Modularization of Artificial Neural Networks
  • Automatic Modularization of Artificial Neural Networks (en)
skos:prefLabel
  • Automatic Modularization of Artificial Neural Networks
  • Automatic Modularization of Artificial Neural Networks (en)
skos:notation
  • RIV/61988987:17310/10:A1100YDH!RIV11-MSM-17310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 248190
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17310/10:A1100YDH
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • artificial neural networks; modular architecture; comparative study (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [81DEB0D6576B]
http://linked.open...v/mistoKonaniAkce
  • Funchal
http://linked.open...i/riv/mistoVydani
  • Portugal
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing, ANNIIP
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Volná, Eva
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • In conjunction with ICINCO 2010.
https://schema.org/isbn
  • 978-989-8425-03-4
http://localhost/t...ganizacniJednotka
  • 17310
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 77 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software