{"id":"https://openalex.org/W7128617849","doi":"https://doi.org/10.1109/access.2026.3663928","title":"Bridging KANs and Tabular Deep Learning: Feature Embeddings and Efficient Ensembling","display_name":"Bridging KANs and Tabular Deep Learning: Feature Embeddings and Efficient Ensembling","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128617849","doi":"https://doi.org/10.1109/access.2026.3663928"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3663928","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3663928","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3663928","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074678084","display_name":"Georgii P. Bulgakov","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098440","display_name":"Ministry of Health","ror":"https://ror.org/00y6q9n79","country_code":"ES","type":"government","lineage":["https://openalex.org/I4210098440"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Georgii Bulgakov","raw_affiliation_strings":["N. N. Blokhin National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation (N. N. Blokhin NMRCO), Moscow, Russia"],"raw_orcid":"https://orcid.org/0009-0002-4703-9642","affiliations":[{"raw_affiliation_string":"N. N. Blokhin National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation (N. N. Blokhin NMRCO), Moscow, Russia","institution_ids":["https://openalex.org/I4210098440"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125637266","display_name":"Danil Rudenko","orcid":null},"institutions":[{"id":"https://openalex.org/I69295118","display_name":"Independent University of Moscow","ror":"https://ror.org/00ygzyy93","country_code":"RU","type":"education","lineage":["https://openalex.org/I69295118"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Danil Rudenko","raw_affiliation_strings":["Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia","institution_ids":["https://openalex.org/I69295118"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093734244","display_name":"Gleb Molodtsov","orcid":"https://orcid.org/0009-0004-3516-1848"},"institutions":[{"id":"https://openalex.org/I69295118","display_name":"Independent University of Moscow","ror":"https://ror.org/00ygzyy93","country_code":"RU","type":"education","lineage":["https://openalex.org/I69295118"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Gleb Molodtsov","raw_affiliation_strings":["Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia"],"raw_orcid":"https://orcid.org/0009-0004-3516-1848","affiliations":[{"raw_affiliation_string":"Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia","institution_ids":["https://openalex.org/I69295118"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088060268","display_name":"Aleksandr Beznosikov","orcid":"https://orcid.org/0000-0002-3217-3614"},"institutions":[{"id":"https://openalex.org/I69295118","display_name":"Independent University of Moscow","ror":"https://ror.org/00ygzyy93","country_code":"RU","type":"education","lineage":["https://openalex.org/I69295118"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Aleksandr Beznosikov","raw_affiliation_strings":["Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia"],"raw_orcid":"https://orcid.org/0000-0002-3217-3614","affiliations":[{"raw_affiliation_string":"Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia","institution_ids":["https://openalex.org/I69295118"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5074678084"],"corresponding_institution_ids":["https://openalex.org/I4210098440"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2902872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"30073","last_page":"30086"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.14710000157356262,"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"}},"topics":[{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.14710000157356262,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.1200999990105629,"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/T14347","display_name":"Big Data and Digital Economy","score":0.0828000009059906,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/bridging","display_name":"Bridging (networking)","score":0.7753000259399414},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6043999791145325},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5584999918937683},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49970000982284546},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48489999771118164},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44600000977516174},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.44110000133514404},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4361000061035156}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.781499981880188},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7753000259399414},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6043999791145325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5694000124931335},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5584999918937683},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48489999771118164},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3921999931335449},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3237999975681305},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2892000079154968},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.28200000524520874},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3663928","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3663928","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3663928","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3663928","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.41208410263061523,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G7640686651","display_name":null,"funder_award_id":"139-15-2025-008","funder_id":"https://openalex.org/F4320329807","funder_display_name":"Ministry of Health of the Russian Federation"}],"funders":[{"id":"https://openalex.org/F4320329807","display_name":"Ministry of Health of the Russian Federation","ror":"https://ror.org/01p8ehb87"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Despite":[0],"recently":[1],"achieving":[2],"breakthrough":[3],"performance":[4],"on":[5,41,131],"tabular":[6,50,63,133],"data,":[7],"MLP-based":[8],"approaches":[9],"like":[10],"TabM":[11],"still":[12],"miss":[13],"complex":[14],"feature":[15,72],"interactions,":[16],"leaving":[17],"room":[18],"for":[19,82,126,148],"further":[20],"improvement.":[21],"Meanwhile,":[22],"Kolmogorov-Arnold":[23],"Networks":[24],"(KANs)":[25],"offer":[26],"a":[27],"promising":[28],"alternative":[29],"by":[30],"switching":[31],"from":[32],"weights":[33],"and":[34,71,100,102,119],"fixed":[35],"activations":[36],"to":[37,45,122,146],"learnable":[38],"univariate":[39],"functions":[40],"edges.":[42],"This":[43],"allows":[44],"capture":[46],"intricate":[47],"patterns":[48],"in":[49,58],"data.":[51],"However,":[52],"their":[53],"potential":[54],"remains":[55],"largely":[56],"unexplored":[57],"the":[59,78,89,107,143,149],"context":[60],"of":[61,80,91],"modern":[62,96],"deep":[64],"learning":[65],"\u2013":[66],"particularly":[67],"regarding":[68],"advanced":[69,92],"ensembling":[70,104],"engineering":[73],"techniques.":[74],"We":[75],"substantially":[76],"enhance":[77],"application":[79],"KANs":[81],"structured":[83],"data":[84],"modeling.":[85],"Specifically,":[86],"we":[87,111],"pioneer":[88],"use":[90],"numerical":[93],"embedding":[94],"schemes,":[95],"optimizers":[97],"(e.g.,":[98],"Muon":[99],"AdEMAMix),":[101],"parameter-efficient":[103],"built":[105],"upon":[106],"KAN":[108],"architecture.":[109],"Moreover,":[110],"perform":[112],"an":[113],"ablation":[114],"study":[115],"across":[116,142],"architecture":[117],"design":[118],"key":[120],"hyperparameters":[121],"derive":[123],"concrete":[124],"recommendations":[125],"practitioners.":[127],"Through":[128],"extensive":[129],"evaluation":[130],"public":[132],"benchmarks,":[134],"our":[135],"enhanced":[136],"KAN-based":[137],"architectures":[138],"achieve":[139],"5":[140],"wins":[141],"benchmark,":[144],"compared":[145],"3":[147],"strongest":[150],"MLP":[151],"baseline,":[152],"while":[153],"maintaining":[154],"computational":[155],"efficiency.":[156]},"counts_by_year":[],"updated_date":"2026-03-03T06:13:14.889584","created_date":"2026-02-12T00:00:00"}
