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1、DeepLearninginNeuralNetworks:AnOverviewTechnicalReportIDSIA-03-14/arXiv:1404.7828v4[cs.NE](88pages,888references)JurgenSchmidhuber¨TheSwissAILabIDSIAIstitutoDalleMollediStudisull’IntelligenzaArtificialeUniversityofLugano&SUPSIGalleria2,6928Manno-LuganoSwitzerland8October2014AbstractInre
2、centyears,deepartificialneuralnetworks(includingrecurrentones)havewonnumerouscontestsinpatternrecognitionandmachinelearning.Thishistoricalsurveycompactlysummarisesrelevantwork,muchofitfromthepreviousmillennium.Shallowanddeeplearnersaredistin-guishedbythedepthoftheircreditassignmentpaths
3、,whicharechainsofpossiblylearnable,causallinksbetweenactionsandeffects.Ireviewdeepsupervisedlearning(alsorecapitulatingthehistoryofbackpropagation),unsupervisedlearning,reinforcementlearning&evolutionarycomputation,andindirectsearchforshortprogramsencodingdeepandlargenetworks.LATEXsour
4、ce:http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.texCompleteBIBTEXfile(888kB):http://www.idsia.ch/˜juergen/deep.bibPrefaceThisisthepreprintofaninvitedDeepLearning(DL)overview.OneofitsgoalsistoassigncreditarXiv:1404.7828v4[cs.NE]8Oct2014tothosewhocontributedtothepresentstateoftheart.
5、Iacknowledgethelimitationsofattemptingtoachievethisgoal.TheDLresearchcommunityitselfmaybeviewedasacontinuallyevolving,deepnetworkofscientistswhohaveinfluencedeachotherincomplexways.StartingfromrecentDLresults,Itriedtotracebacktheoriginsofrelevantideasthroughthepasthalfcenturyandbeyond,s
6、ometimesusing“localsearch”tofollowcitationsofcitationsbackwardsintime.SincenotallDLpublicationsproperlyacknowledgeearlierrelevantwork,additionalglobalsearchstrategieswereem-ployed,aidedbyconsultingnumerousneuralnetworkexperts.Asaresult,thepresentpreprintmostlyconsistsofreferences.Never
7、theless,throughanexpertselectionbiasImayhavemissedimportantwork.ArelatedbiaswassurelyintroducedbymyspecialfamiliaritywiththeworkofmyownDLresearchgroupinthepastquarter-century.Forthesereasons,thisworkshouldbeviewedasmerelyasnapshotofanongoingcreditassignmentprocess.Tohelpimproveit,ple
