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1、ItamarArel,DerekC.Rose,ResearchandThomasP.KarnowskiFrontierTheUniversityofTennessee,USADeepMachineLearning—ANewFrontierinArtificialIntelligenceResearchI.Introductionognitionsystemshasshiftedtothetime[5][6].Tothatend,modelingtheimickingtheefficiencyandhuman-engineeredfeatureextractiontemporalc
2、omponentoftheobservationsMrobustnessbywhichthehumanprocess,whichattimescanbechalleng-playsacriticalroleineffectiveinforma-brainrepresentsinformationingandhighlyapplication-dependenttionrepresentation.Capturingspa-hasbeenacorechallengeinarti-narti-[2].Moreover,ifincompleteor[2].Motiotemporalde
3、pendencies,basedonficialintelligenceresearchforforerroneousfeaturesareexerro-regularitiesintheobservations,isthere-decades.Humansareex-tracted,theclassificationtraforeviewedasafundamentalgoalforposedtomyriadofsenso-processisinherentlylim-pdeeplearningsystems.rydatareceivedeveryitedinperforman
4、ce.AssumingrobustdeeplearningissecondofthedayandareRecentneuroscienceachieved,itwouldbepossibletotrainsomehowabletocapturefindingshaveprovidedsuchahierarchicalnetworkonalargesetcriticalaspectsofthisinsightintotheprinci-ofobservationsandlaterextractsignalsdatainawaythatallowsplesgoverninginfor
5、ma-pfromthisnetworktoarelativelysimpleforitsfutureuseinacon-tionrepresentationintheticlassificationengineforthepurposeofcisemanner.Over50yearsrsmammalbrain,leadingtomarobustpatternrecognition.Robustnessago,RichardBellman,whowhonewideasfordesigningsys-newherereferstotheabilitytoexhibitclassi-©
6、BRANDXintroduceddynamicprogrammingiPICTUREStemsthatrepresentinformation.hficationinvariancetoadiverserangeoftheoryandpioneeredthefieldofopti-Oneofthekeyfindingshasbeenthattransformationsanddistortions,includingmalcontrol,assertedthathighdimen-theneocortex,whichisassociatedwithnoise,scale,rota
7、tion,variouslightingsionalityofdataisafundamentalhurdlemanycognitiveabilities,doesnotexplic-conditions,displacement,etc.inmanyscienceandengineeringappli-itlypre-processsensorysignals,butrath-Thisarticleprovidesanoverviewofcations.Themaindiffi
