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1、UnderstandingDeepConvolutionalNetworksStephaneMallatEcoleNormaleSuperieure,CNRS,PSL45rued'Ulm,75005Paris,FranceToappearinPhilosophicalTransactionsAin2016AbstractDeepconvolutionalnetworksprovidestateoftheartclassicationsandregressionsresultsovermanyhigh-dimensional
2、problems.Wereviewtheirarchitecture,whichscattersdatawithacascadeoflinearlterweightsandnon-linearities.Amathematicalframeworkisintroducedtoanalyzetheirproperties.Computationsofinvariantsinvolvemultiscalecontractions,thelinearizationofhierarchicalsymmetries,andsparsese
3、parations.Applicationsarediscussed.x1IntroductionSupervisedlearningisahigh-dimensionalinterpolationproblem.Weapproximateafunctionf(x)fromqtrainingsamplesfxi;f(xi)g,wherexisadatavectorofveryhighdimensiond.Thisdimensionisofteniqlargerthan106,forimagesorotherlargesizesi
4、gnals.Deepconvolutionalneuralnetworkshaverecentlyobtainedremarkableexperimentalresults[21].Theygivestateoftheartperformancesforimageclassicationwiththousandsofcomplexclasses[19],speechrecognition[17],bio-medicalapplications[22],naturallanguageunderstanding[30],andinm
5、anyotherdomains.Theyarealsostudiedasneuro-physiologicalmodelsofvision[4].Multilayerneuralnetworksarecomputationallearningarchitectureswhichpropagatetheinputdataacrossasequenceoflinearoperatorsandsimplenon-linearities.Thepropertiesofshallownetworks,withonehiddenlayer,a
6、rewellunderstoodasdecompositionsinfamiliesofridgefunctions[10].However,theseapproachesdonotextendtonetworkswithmorelayers.Deepconvolutionalneuralnetworks,introducedbyarXiv:1601.04920v1[stat.ML]19Jan2016LeCun[20],areimplementedwithlinearconvolutionsfollowedbynon-linear
7、ities,overtypicallymorethan5layers.Thesecomplexprogrammablemachines,denedbypotentiallybillionsoflterweights,bringustoadierentmathematicalworld.Manyresearchershavepointedoutthatdeepconvolutionnetworksarecomputingprogressivelymorepowerfulinvariantsasdepthincreases[4,
8、21],butrelationswithnetworksweightsandnon-linearitiesarecomplex.Thispaperaimsatclarifyingimportantprincipleswhichgovernthepr