{"id":"https://openalex.org/W7225035482","doi":"https://doi.org/10.62411/faith.3048-3719-449","title":"Revisiting Multilayer Perceptrons in the Era of Kolmogorov Arnold Networks: An Empirical Comparison on Image Classification","display_name":"Revisiting Multilayer Perceptrons in the Era of Kolmogorov Arnold Networks: An Empirical Comparison on Image Classification","publication_year":2026,"publication_date":"2026-11-01","ids":{"openalex":"https://openalex.org/W7225035482","doi":"https://doi.org/10.62411/faith.3048-3719-449"},"language":"en","primary_location":{"id":"doi:10.62411/faith.3048-3719-449","doi":"https://doi.org/10.62411/faith.3048-3719-449","is_oa":true,"landing_page_url":"https://doi.org/10.62411/faith.3048-3719-449","pdf_url":null,"source":{"id":"https://openalex.org/S4404663256","display_name":"Journal of Future Artificial Intelligence and Technologies","issn_l":"3048-3719","issn":["3048-3719"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Future Artificial Intelligence and Technologies","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.62411/faith.3048-3719-449","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119806849","display_name":"Munyao Muia","orcid":"https://orcid.org/0009-0004-6781-3820","observed_orcids":["https://orcid.org/0009-0004-6781-3820"]},"institutions":[{"id":"https://openalex.org/I3133159679","display_name":"Murang'a University of Technology","ror":"https://ror.org/057k4ej49","country_code":"KE","type":"education","lineage":["https://openalex.org/I3133159679"]}],"countries":["KE"],"is_corresponding":false,"raw_author_name":"Munyao Muia","raw_affiliation_strings":["Murang'a University of Technology"],"raw_orcid":"https://orcid.org/0009-0004-6781-3820","affiliations":[{"raw_affiliation_string":"Murang'a University of Technology","institution_ids":["https://openalex.org/I3133159679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119574249","display_name":"Geoffry Mutiso","orcid":"https://orcid.org/0009-0009-8623-7853","observed_orcids":["https://orcid.org/0009-0009-8623-7853"]},"institutions":[{"id":"https://openalex.org/I3133159679","display_name":"Murang'a University of Technology","ror":"https://ror.org/057k4ej49","country_code":"KE","type":"education","lineage":["https://openalex.org/I3133159679"]}],"countries":["KE"],"is_corresponding":false,"raw_author_name":"Geoffry Mutiso","raw_affiliation_strings":["Murang'a University of Technology"],"raw_orcid":"https://orcid.org/0009-0009-8623-7853","affiliations":[{"raw_affiliation_string":"Murang'a University of Technology","institution_ids":["https://openalex.org/I3133159679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068044468","display_name":"Stephen Kahara Wanjau","orcid":"https://orcid.org/0000-0002-6567-6912","observed_orcids":["https://orcid.org/0000-0002-6567-6912"]},"institutions":[{"id":"https://openalex.org/I3133159679","display_name":"Murang'a University of Technology","ror":"https://ror.org/057k4ej49","country_code":"KE","type":"education","lineage":["https://openalex.org/I3133159679"]}],"countries":["KE"],"is_corresponding":false,"raw_author_name":"Stephen Kahara","raw_affiliation_strings":["Murang'a University of Technology"],"raw_orcid":"https://orcid.org/0000-0002-6567-6912","affiliations":[{"raw_affiliation_string":"Murang'a University of Technology","institution_ids":["https://openalex.org/I3133159679"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3133159679"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":"3","first_page":"526","last_page":"545"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6259999871253967,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6259999871253967,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.3402999937534332,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.013899999670684338,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.9330000281333923},{"id":"https://openalex.org/keywords/kolmogorov-arnold-networks","display_name":"Kolmogorov-Arnold networks","score":0.9629999995231628},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.8090000152587891},{"id":"https://openalex.org/keywords/model-capacity","display_name":"model capacity","score":0.8069999814033508},{"id":"https://openalex.org/keywords/cifar-10","display_name":"CIFAR-10","score":0.7979999780654907},{"id":"https://openalex.org/keywords/training-efficiency","display_name":"training efficiency","score":0.7020000219345093},{"id":"https://openalex.org/keywords/convergence-rate","display_name":"convergence rate","score":0.6290000081062317}],"concepts":[{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.7526000142097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5701000094413757},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5020999908447266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47290000319480896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46630001068115234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45320001244544983},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.42980000376701355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.36579999327659607},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3111000061035156},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2784000039100647},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25099998712539673},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.62411/faith.3048-3719-449","doi":"https://doi.org/10.62411/faith.3048-3719-449","is_oa":true,"landing_page_url":"https://doi.org/10.62411/faith.3048-3719-449","pdf_url":null,"source":{"id":"https://openalex.org/S4404663256","display_name":"Journal of Future Artificial Intelligence and Technologies","issn_l":"3048-3719","issn":["3048-3719"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Future Artificial Intelligence and Technologies","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.62411/faith.3048-3719-449","doi":"https://doi.org/10.62411/faith.3048-3719-449","is_oa":true,"landing_page_url":"https://doi.org/10.62411/faith.3048-3719-449","pdf_url":null,"source":{"id":"https://openalex.org/S4404663256","display_name":"Journal of Future Artificial Intelligence and Technologies","issn_l":"3048-3719","issn":["3048-3719"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Future Artificial Intelligence and Technologies","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2011306557","https://openalex.org/W4384807847","https://openalex.org/W4393905284","https://openalex.org/W4396821501","https://openalex.org/W4399424696","https://openalex.org/W4399457380","https://openalex.org/W4399695600","https://openalex.org/W4399912438","https://openalex.org/W4399990872","https://openalex.org/W4400332993","https://openalex.org/W4400340474","https://openalex.org/W4401203083","https://openalex.org/W4402115486","https://openalex.org/W4403885551","https://openalex.org/W4405029397","https://openalex.org/W4412743805","https://openalex.org/W4414204244","https://openalex.org/W4414594717","https://openalex.org/W4414971181","https://openalex.org/W6891371962","https://openalex.org/W7117483690","https://openalex.org/W7133133453","https://openalex.org/W7147542742","https://openalex.org/W7150153381","https://openalex.org/W7161853866","https://openalex.org/W7164955272","https://openalex.org/W7169113550","https://openalex.org/W7196946602"],"related_works":["https://openalex.org/W7154845627","https://openalex.org/W7154612135","https://openalex.org/W4403702901","https://openalex.org/W7169113550","https://openalex.org/W4411958949","https://openalex.org/W7150153381","https://openalex.org/W7132910470","https://openalex.org/W7124469266","https://openalex.org/W4416745548","https://openalex.org/W4404389467"],"abstract_inverted_index":{"Multilayer":[0],"perceptrons":[1],"(MLPs)":[2],"remain":[3],"a":[4,65,166,249],"widely":[5],"used":[6],"architecture":[7,185],"in":[8,35,53,93,180,194,204],"deep":[9],"learning,":[10],"whereas":[11,236],"Kolmogorov\u2013Arnold":[12],"networks":[13],"(KANs)":[14],"have":[15],"emerged":[16],"as":[17,137],"an":[18,105],"alternative":[19],"that":[20,116,135,213],"represents":[21],"nonlinear":[22],"transformations":[23],"through":[24],"learnable":[25],"univariate":[26],"functions,":[27],"commonly":[28],"parameterized":[29],"using":[30],"splines.":[31],"Despite":[32],"growing":[33],"interest":[34],"KANs,":[36],"their":[37],"performance":[38],"relative":[39,215],"to":[40,82,90,103],"MLPs":[41,70,124,146,155,190,218,237],"on":[42,73,222],"high-dimensional":[43],"image":[44],"classification":[45,121],"tasks":[46],"remains":[47],"insufficiently":[48],"characterized,":[49],"partly":[50],"because":[51],"differences":[52,109,179],"model":[54,86,138,223],"capacity":[55,77,111,139,144,224],"and":[56,71,170,200,219,225],"training":[57,98],"protocols":[58],"complicate":[59],"direct":[60],"comparisons.":[61],"This":[62],"study":[63],"presents":[64],"parameter-controlled":[66],"empirical":[67],"comparison":[68],"of":[69,107,131,217,252],"KANs":[72,117,149,201,220,228],"CIFAR-10":[74],"across":[75,110,242],"five":[76],"tiers":[78],"ranging":[79],"from":[80],"64K":[81],"4M":[83],"parameters.":[84],"The":[85,113],"configurations":[87],"are":[88],"matched":[89],"within":[91],"2%":[92],"parameter":[94],"count,":[95],"while":[96],"the":[97,142,181,210,214,253,257],"protocol":[99],"is":[100],"held":[101],"consistent":[102],"facilitate":[104],"assessment":[106,251],"architectural":[108],"levels.":[112],"results":[114,176,247],"show":[115],"achieve":[118],"higher":[119,160],"mean":[120],"accuracy":[122,161,231],"than":[123],"at":[125,233],"low-to-medium":[126],"capacities":[127],"(T\u2081\u2013T\u2084),":[128],"with":[129,189,196],"gains":[130,203,232],"0.3\u20131.1":[132],"percentage":[133,152],"points":[134],"decrease":[136],"increases.":[140],"At":[141],"highest":[143],"(T\u2085),":[145],"marginally":[147],"outperform":[148],"by":[150,165],"0.3":[151],"points.":[153],"However,":[154],"consistently":[156],"deliver":[157],"3.0\u20135.3":[158],"times":[159],"per":[162],"GPU-hour,":[163],"supported":[164],"3.1\u20135.4-fold":[167],"training-throughput":[168],"advantage":[169],"18\u201327":[171],"fewer":[172],"convergence":[173],"epochs.":[174],"Per-class":[175],"further":[177],"reveal":[178],"categories":[182],"where":[183],"each":[184],"performs":[186],"relatively":[187],"well,":[188],"showing":[191],"marginal":[192],"advantages":[193,216],"classes":[195],"lower":[197,234],"intra-class":[198],"variability":[199],"exhibiting":[202],"visually":[205],"complex,":[206],"higher-variability":[207],"classes.":[208],"Overall,":[209],"findings":[211],"suggest":[212],"depend":[221],"computational":[226,240],"constraints:":[227],"offer":[229],"modest":[230],"capacities,":[235],"provide":[238],"greater":[239],"efficiency":[241],"all":[243],"evaluated":[244],"tiers.":[245],"These":[246],"support":[248],"complementary":[250],"two":[254],"architectures":[255],"under":[256],"specific":[258],"experimental":[259],"conditions":[260],"considered.":[261]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-11-02T00:00:00"}