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1、DeepLearninginNeuralNetworks:AnOverviewTechnicalReportIDSIA-03-14/arXiv:1404.7828v4[cs.NE](88pages,888references)JurgenSchmidhuber¨TheSwissAILabIDSIAIstitutoDalleMollediStudisull’IntelligenzaArtificialeUniversityofLugano&SUPSIGalleria2,6928Manno-LuganoSwitzerland8October201
2、4AbstractInrecentyears,deepartificialneuralnetworks(includingrecurrentones)havewonnumerouscontestsinpatternrecognitionandmachinelearning.Thishistoricalsurveycompactlysummarisesrelevantwork,muchofitfromthepreviousmillennium.Shallowanddeeplearnersaredistin-guishedbythedepthof
3、theircreditassignmentpaths,whicharechainsofpossiblylearnable,causallinksbetweenactionsandeffects.Ireviewdeepsupervisedlearning(alsorecapitulatingthehistoryofbackpropagation),unsupervisedlearning,reinforcementlearning&evolutionarycomputation,andindirectsearchforshortprogram
4、sencodingdeepandlargenetworks.LATEXsource:http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.texCompleteBIBTEXfile(888kB):http://www.idsia.ch/˜juergen/deep.bibPrefaceThisisthepreprintofaninvitedDeepLearning(DL)overview.OneofitsgoalsistoassigncreditarXiv:1404.7828v4[cs.NE]8Oc
5、t2014tothosewhocontributedtothepresentstateoftheart.Iacknowledgethelimitationsofattemptingtoachievethisgoal.TheDLresearchcommunityitselfmaybeviewedasacontinuallyevolving,deepnetworkofscientistswhohaveinfluencedeachotherincomplexways.StartingfromrecentDLresults,Itriedtotrace
6、backtheoriginsofrelevantideasthroughthepasthalfcenturyandbeyond,sometimesusing“localsearch”tofollowcitationsofcitationsbackwardsintime.SincenotallDLpublicationsproperlyacknowledgeearlierrelevantwork,additionalglobalsearchstrategieswereem-ployed,aidedbyconsultingnumerousneu
7、ralnetworkexperts.Asaresult,thepresentpreprintmostlyconsistsofreferences.Nevertheless,throughanexpertselectionbiasImayhavemissedimportantwork.ArelatedbiaswassurelyintroducedbymyspecialfamiliaritywiththeworkofmyownDLresearchgroupinthepastquarter-century.Forthesereasons,this
8、workshouldbeviewedasmerelyasnapshotofanongoingcreditassignmentprocess.Tohelpimproveit,ple
