Modélisation stochastique d'une population de neurones, méta-apprentissage dans un problème de classification
Identifieur interne : 006156 ( Hal/Curation ); précédent : 006155; suivant : 006157Modélisation stochastique d'une population de neurones, méta-apprentissage dans un problème de classification
Auteurs : Bruno Scherrer [France] ; Frédéric Alexandre [France] ; François Charpillet [France] ; Stéphane VialleSource :
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Abstract
La modélisation stochastique d'une population de neurones peut s'inspirer de l'étude générale de populations d'entités en interactions. l'étude stochastique de telles populations d'agents a été abordée par une famille de méthodes dérivées de la recherche opérationnelle et de récents travaux concernant l'apprentissage par renforcement dans de tels systèmes peuvent servir d'outil pour la modélisation de neurones en interaction. Nous présentons un cadre générique d'étude stochastique d'agents partageant un environnement et une méthode d'apprentissage par renforcement qui lui est associée. Nous exposons ensuite l'état actuel de nos recherches concernant des possibles adaptations au paradigme neuronal pour un problème de classification.
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type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Vialle, Stephane" sort="Vialle, Stephane" uniqKey="Vialle S" first="Stéphane" last="Vialle">Stéphane Vialle</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>classification</term><term>meta-learning</term><term>modélisation stochastique</term><term>méta-apprentissage</term><term>neural networks</term><term>réseaux de neurones</term><term>stochastic modeling</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">La modélisation stochastique d'une population de neurones peut s'inspirer de l'étude générale de populations d'entités en interactions. l'étude stochastique de telles populations d'agents a été abordée par une famille de méthodes dérivées de la recherche opérationnelle et de récents travaux concernant l'apprentissage par renforcement dans de tels systèmes peuvent servir d'outil pour la modélisation de neurones en interaction. Nous présentons un cadre générique d'étude stochastique d'agents partageant un environnement et une méthode d'apprentissage par renforcement qui lui est associée. Nous exposons ensuite l'état actuel de nos recherches concernant des possibles adaptations au paradigme neuronal pour un problème de classification.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Modélisation stochastique d'une population de neurones, méta-apprentissage dans un problème de classification</title><author role="aut"><persName><forename type="first">Bruno</forename><surname>Scherrer</surname></persName><email>scherrer@loria.fr</email><idno type="idhal">bruno-scherrer</idno><idno type="halauthor">130303</idno><idno type="IdRef">http://www.idref.fr/073360708</idno><orgName ref="#struct-364646"></orgName><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Frédéric</forename><surname>Alexandre</surname></persName><email>Frederic.Alexandre@inria.fr</email><idno type="idhal">frederic-alexandre</idno><idno type="halauthor">778506</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">François</forename><surname>Charpillet</surname></persName><email>francois.charpillet@loria.fr</email><idno type="idhal">francois-charpillet</idno><idno type="halauthor">129571</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2355"></affiliation></author><author role="aut"><persName><forename type="first">Stéphane</forename><surname>Vialle</surname></persName><email></email><idno type="idhal">stephane-vialle</idno><idno type="halauthor">129827</idno><orgName ref="#struct-300812"></orgName></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 08:51:04</date><date type="whenModified">2016-05-18 09:00:03</date><date type="whenReleased">2006-09-28 15:22:45</date><date type="whenProduced">2000</date></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00099114</idno><idno type="halUri">https://hal.inria.fr/inria-00099114</idno><idno type="halBibtex">scherrer:inria-00099114</idno><idno type="halRefHtml">Neurosciences et sciences de l'ingénieur, 2000, Dinard, France, 4 p, 2000</idno><idno type="halRef">Neurosciences et sciences de l'ingénieur, 2000, Dinard, France, 4 p, 2000</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes et comité de lecture. nationale.</note><note type="audience" n="3">National</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Modélisation stochastique d'une population de neurones, méta-apprentissage dans un problème de classification</title><author role="aut"><persName><forename type="first">Bruno</forename><surname>Scherrer</surname></persName><email>scherrer@loria.fr</email><idno type="idHal">bruno-scherrer</idno><idno type="halAuthorId">130303</idno><idno type="IdRef">http://www.idref.fr/073360708</idno><orgName ref="#struct-364646"></orgName><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Frédéric</forename><surname>Alexandre</surname></persName><email>Frederic.Alexandre@inria.fr</email><idno type="idHal">frederic-alexandre</idno><idno type="halAuthorId">778506</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">François</forename><surname>Charpillet</surname></persName><email>francois.charpillet@loria.fr</email><idno type="idHal">francois-charpillet</idno><idno type="halAuthorId">129571</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2355"></affiliation></author><author role="aut"><persName><forename type="first">Stéphane</forename><surname>Vialle</surname></persName><idno type="idHal">stephane-vialle</idno><idno type="halAuthorId">129827</idno><orgName ref="#struct-300812"></orgName></author></analytic><monogr><idno type="localRef">A00-R-209 || scherrer00a</idno><meeting><title>Neurosciences et sciences de l'ingénieur</title><date type="start">2000</date><settlement>Dinard, France</settlement></meeting><imprint><biblScope unit="pp">4 p</biblScope><date type="datePub">2000</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">modélisation stochastique</term><term xml:lang="fr">neural networks</term><term xml:lang="fr">stochastic modeling</term><term xml:lang="fr">meta-learning</term><term xml:lang="fr">classification</term><term xml:lang="fr">méta-apprentissage</term><term xml:lang="fr">réseaux de neurones</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="fr">La modélisation stochastique d'une population de neurones peut s'inspirer de l'étude générale de populations d'entités en interactions. l'étude stochastique de telles populations d'agents a été abordée par une famille de méthodes dérivées de la recherche opérationnelle et de récents travaux concernant l'apprentissage par renforcement dans de tels systèmes peuvent servir d'outil pour la modélisation de neurones en interaction. Nous présentons un cadre générique d'étude stochastique d'agents partageant un environnement et une méthode d'apprentissage par renforcement qui lui est associée. Nous exposons ensuite l'état actuel de nos recherches concernant des possibles adaptations au paradigme neuronal pour un problème de classification.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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