Publication:
Studying generalization performance of random feature model through equivalent models

dc.contributor.departmentKUIS AI (Koç University & İş Bank Artificial Intelligence Center)
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorDoğan, Zafer
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteResearch Center
dc.date.accessioned2025-03-06T20:57:15Z
dc.date.issued2024
dc.description.abstractRandom Feature Model (RFM) has been received significant attention due to its similarity to neural networks and its relatively simple analysis. The training and generalization performances of the RFM has been shown to be equivalent to the noisy linear model under isotropic data assumption in the asymptotic limit. It is possible to analyze the performance of the RFM using the equivalent model. In this work, we focus on studying the generalization performance of the RFM using equivalent models and extending it to anisotropic data. First, we illustrate the regimes where the equivalence with the aforementioned linear model holds and where it does not hold on Fashion-MNIST dataset. Then, we observe a new equivalence for a regime where the aforementioned equivalence does not hold.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.1109/SIU61531.2024.10600866
dc.identifier.isbn9798350388978
dc.identifier.isbn9798350388961
dc.identifier.issn2165-0608
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85200890989
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600866
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27169
dc.identifier.wos1297894700122
dc.keywordsRandom feature model
dc.keywordsGaussian equivalence
dc.keywordsUniversality
dc.language.isotur
dc.publisherIEEE
dc.relation.ispartof32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024
dc.subjectComputer science
dc.subjectElectrical and electronic
dc.subjectTelecommunications
dc.titleStudying generalization performance of random feature model through equivalent models
dc.title.alternativeDenk modeller kullanarak rasgele öznitelik modelinin genelleme performansının incelenmesi
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorDoğan, Zafer
local.contributor.kuauthorDemir Samet
local.publication.orgunit1College of Engineering
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1Research Center
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2KUIS AI (Koç University & İş Bank Artificial Intelligence Center)
local.publication.orgunit2Graduate School of Sciences and Engineering
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