Publikation:

Discrete-Time Signatures and Randomness in Reservoir Computing

Lade...
Vorschaubild

Dateien

Cuchiero_2-zpejktn8beud5.pdf
Cuchiero_2-zpejktn8beud5.pdfGröße: 771.61 KBDownloads: 58

Datum

2021

Autor:innen

Cuchiero, Christa
Gonon, Lukas
Ortega, Juan-Pablo
Teichmann, Josef

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

IEEE Transactions on Neural Networks and Learning Systems. IEEE. eISSN 2162-237X. Available under: doi: 10.1109/TNNLS.2021.3076777

Zusammenfassung

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is presented. RC is understood in the literature as the possibility of approximating input-output systems with randomly chosen recurrent neural systems and a trained linear readout layer. Light is shed on this phenomenon by constructing what is called strongly universal reservoir systems as random projections of a family of state-space systems that generate Volterra series expansions. This procedure yields a state-affine reservoir system with randomly generated coefficients in a dimension that is logarithmically reduced with respect to the original system. This reservoir system is able to approximate any element in the fading memory filters class just by training a different linear readout for each different filter. Explicit expressions for the probability distributions needed in the generation of the projected reservoir system are stated, and bounds for the committed approximation error are provided.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

Schlagwörter

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690CUCHIERO, Christa, Lukas GONON, Lyudmila GRIGORYEVA, Juan-Pablo ORTEGA, Josef TEICHMANN, 2021. Discrete-Time Signatures and Randomness in Reservoir Computing. In: IEEE Transactions on Neural Networks and Learning Systems. IEEE. eISSN 2162-237X. Available under: doi: 10.1109/TNNLS.2021.3076777
BibTex
@article{Cuchiero2021-05-26Discr-55523,
  year={2021},
  doi={10.1109/TNNLS.2021.3076777},
  title={Discrete-Time Signatures and Randomness in Reservoir Computing},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  author={Cuchiero, Christa and Gonon, Lukas and Grigoryeva, Lyudmila and Ortega, Juan-Pablo and Teichmann, Josef}
}
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/123456789/55523">
    <dc:contributor>Grigoryeva, Lyudmila</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55523/1/Cuchiero_2-zpejktn8beud5.pdf"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-11-12T10:08:41Z</dcterms:available>
    <dc:contributor>Ortega, Juan-Pablo</dc:contributor>
    <dcterms:abstract xml:lang="eng">A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is presented. RC is understood in the literature as the possibility of approximating input-output systems with randomly chosen recurrent neural systems and a trained linear readout layer. Light is shed on this phenomenon by constructing what is called strongly universal reservoir systems as random projections of a family of state-space systems that generate Volterra series expansions. This procedure yields a state-affine reservoir system with randomly generated coefficients in a dimension that is logarithmically reduced with respect to the original system. This reservoir system is able to approximate any element in the fading memory filters class just by training a different linear readout for each different filter. Explicit expressions for the probability distributions needed in the generation of the projected reservoir system are stated, and bounds for the committed approximation error are provided.</dcterms:abstract>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:issued>2021-05-26</dcterms:issued>
    <dc:creator>Cuchiero, Christa</dc:creator>
    <dc:creator>Teichmann, Josef</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Gonon, Lukas</dc:creator>
    <dc:contributor>Cuchiero, Christa</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55523/1/Cuchiero_2-zpejktn8beud5.pdf"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55523"/>
    <dc:contributor>Gonon, Lukas</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-11-12T10:08:41Z</dc:date>
    <dc:creator>Ortega, Juan-Pablo</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:title>Discrete-Time Signatures and Randomness in Reservoir Computing</dcterms:title>
    <dc:creator>Grigoryeva, Lyudmila</dc:creator>
    <dc:contributor>Teichmann, Josef</dc:contributor>
  </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
Nein
Diese Publikation teilen