Publikation:

Time-Delay Reservoir Computers and High-Speed Information Processing Capacity

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2016

Autor:innen

Henriques, Julie
Larger, Laurent
Ortega, Juan-Pablo

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

Proceedings : 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing, 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science. Piscataway, NJ: IEEE, 2016, pp. 492-495. ISBN 978-1-5090-3593-9. Available under: doi: 10.1109/CSE-EUC-DCABES.2016.230

Zusammenfassung

The aim of this presentation is to show how various ideas coming from the nonlinear stability theory of functional differential systems, stochastic modeling, and machine learning, can be put together in order to create an approximating model that explains the working mechanisms behind a certain type of reservoir computers. Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus on time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. The reservoir design problem is addressed, which remains the biggest challenge in the applicability of this information processing scheme. Our results use the information available regarding the optimal reservoir working regimes in order to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

Schlagwörter

Konferenz

IEEE International Conference on Computational Science and Engineering (CSE 2016), 24. Aug. 2016 - 26. Aug. 2016, Paris, France
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690GRIGORYEVA, Lyudmila, Julie HENRIQUES, Laurent LARGER, Juan-Pablo ORTEGA, 2016. Time-Delay Reservoir Computers and High-Speed Information Processing Capacity. IEEE International Conference on Computational Science and Engineering (CSE 2016). Paris, France, 24. Aug. 2016 - 26. Aug. 2016. In: Proceedings : 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing, 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science. Piscataway, NJ: IEEE, 2016, pp. 492-495. ISBN 978-1-5090-3593-9. Available under: doi: 10.1109/CSE-EUC-DCABES.2016.230
BibTex
@inproceedings{Grigoryeva2016-08TimeD-40645,
  year={2016},
  doi={10.1109/CSE-EUC-DCABES.2016.230},
  title={Time-Delay Reservoir Computers and High-Speed Information Processing Capacity},
  isbn={978-1-5090-3593-9},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={Proceedings : 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing, 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science},
  pages={492--495},
  author={Grigoryeva, Lyudmila and Henriques, Julie and Larger, Laurent and Ortega, Juan-Pablo}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/40645">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dc:creator>Larger, Laurent</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dc:language>eng</dc:language>
    <dc:creator>Grigoryeva, Lyudmila</dc:creator>
    <dc:creator>Henriques, Julie</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/40645"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-11-16T13:26:13Z</dc:date>
    <dcterms:abstract xml:lang="eng">The aim of this presentation is to show how various ideas coming from the nonlinear stability theory of functional differential systems, stochastic modeling, and machine learning, can be put together in order to create an approximating model that explains the working mechanisms behind a certain type of reservoir computers. Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus on time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. The reservoir design problem is addressed, which remains the biggest challenge in the applicability of this information processing scheme. Our results use the information available regarding the optimal reservoir working regimes in order to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.</dcterms:abstract>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-11-16T13:26:13Z</dcterms:available>
    <dc:contributor>Larger, Laurent</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Time-Delay Reservoir Computers and High-Speed Information Processing Capacity</dcterms:title>
    <dcterms:issued>2016-08</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Ortega, Juan-Pablo</dc:contributor>
    <dc:contributor>Grigoryeva, Lyudmila</dc:contributor>
    <dc:contributor>Henriques, Julie</dc:contributor>
    <dc:creator>Ortega, Juan-Pablo</dc:creator>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen