000775908 001__ 775908 000775908 005__ 20260113092241.0 000775908 037__ $$aRWTH-2020-00313 000775908 041__ $$aEnglish 000775908 1001_ $$0P:(DE-82)IDM00024$$aHerty, Michael$$b0$$urwth 000775908 245__ $$aKinetic Theory for Residual Neural Networks$$honline 000775908 260__ $$c2020 000775908 3367_ $$028$$2EndNote$$aElectronic Article 000775908 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1578557455_10878 000775908 3367_ $$2BibTeX$$aARTICLE 000775908 3367_ $$2DRIVER$$apreprint 000775908 3367_ $$2DataCite$$aOutput Types/Working Paper 000775908 3367_ $$2ORCID$$aWORKING_PAPER 000775908 591__ $$aGermany 000775908 653_7 $$aKeywords Residual neural network 000775908 653_7 $$acontinuous limit 000775908 653_7 $$akinetic equation 000775908 653_7 $$amachine learning application 000775908 653_7 $$amean field equation 000775908 7001_ $$0P:(DE-82)IDM02610$$aTrimborn, Torsten$$b1$$urwth 000775908 7001_ $$0P:(DE-82)IDM02869$$aVisconti, Giuseppe$$b2$$urwth 000775908 8564_ $$uhttps://www.igpm.rwth-aachen.de/forschung/preprints/500$$yFulltext 000775908 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM00024$$aRWTH Aachen$$b0$$kRWTH 000775908 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM02610$$aRWTH Aachen$$b1$$kRWTH 000775908 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM02869$$aRWTH Aachen$$b2$$kRWTH 000775908 9141_ $$y2020 000775908 9201_ $$0I:(DE-82)114620_20140620$$k114620$$lLehr- und Forschungsgebiet Mathematik$$x0 000775908 9201_ $$0I:(DE-82)110000_20140620$$k110000$$lFachgruppe Mathematik$$x1 000775908 961__ $$c2020-01-09T09:12:29.856725$$x2020-01-09T09:12:29.856725$$z2020-01-09 000775908 980__ $$aI:(DE-82)110000_20140620 000775908 980__ $$aI:(DE-82)114620_20140620 000775908 980__ $$aUNRESTRICTED 000775908 980__ $$aVDBINPRINT 000775908 980__ $$apreprint