{"id":"https://openalex.org/W4416799489","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11248995","title":"Kernel Ridge Regression for Efficient Learning of High-Capacity Hopfield Networks","display_name":"Kernel Ridge Regression for Efficient Learning of High-Capacity Hopfield Networks","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4416799489","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11248995"},"language":null,"primary_location":{"id":"doi:10.1109/apsipaasc65261.2025.11248995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11248995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039826522","display_name":"Akira Tamamori","orcid":null},"institutions":[{"id":"https://openalex.org/I190508380","display_name":"Aichi Institute of Technology","ror":"https://ror.org/02qsepw74","country_code":"JP","type":"education","lineage":["https://openalex.org/I190508380"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Akira Tamamori","raw_affiliation_strings":["Aichi Institute of Technology,Japan"],"affiliations":[{"raw_affiliation_string":"Aichi Institute of Technology,Japan","institution_ids":["https://openalex.org/I190508380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5039826522"],"corresponding_institution_ids":["https://openalex.org/I190508380"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39836091,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1212","last_page":"1217"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.4350000023841858,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.4350000023841858,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.20469999313354492,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.12330000102519989,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/kernel","display_name":"Kernel (algebra)","score":0.652999997138977},{"id":"https://openalex.org/keywords/hopfield-network","display_name":"Hopfield network","score":0.5194000005722046},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46889999508857727},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.4503999948501587},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.3986999988555908},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.39010000228881836},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3878999948501587},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.374099999666214},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.37130001187324524}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.652999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6266999840736389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6222000122070312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5364000201225281},{"id":"https://openalex.org/C46421273","wikidata":"https://www.wikidata.org/wiki/Q1407668","display_name":"Hopfield network","level":3,"score":0.5194000005722046},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.4503999948501587},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.3986999988555908},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3878999948501587},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.374099999666214},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.37130001187324524},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35989999771118164},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.35530000925064087},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3034000098705292},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.2896000146865845},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26190000772476196},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc65261.2025.11248995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11248995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1560724230","https://openalex.org/W2080792322","https://openalex.org/W4237059196","https://openalex.org/W4413275104"],"related_works":[],"abstract_inverted_index":{"Hopfield":[0,56,175],"networks":[1],"using":[2],"Hebbian":[3],"learning":[4,54,76],"suffer":[5],"from":[6],"limited":[7],"storage":[8,30,98,102,133],"capacity.":[9],"While":[10],"supervised":[11],"methods":[12,22],"like":[13,23],"Linear":[14],"Logistic":[15,25],"Regression":[16,26,46],"(LLR)":[17],"offer":[18],"some":[19],"improvement,":[20],"kernel":[21,61],"Kernel":[24,44],"(KLR)":[27],"significantly":[28,126],"enhance":[29],"capacity":[31,99],"and":[32,63,82,89,106,125,141,169],"noise":[33,107],"robustness.":[34],"However,":[35],"KLR":[36,154,170],"requires":[37],"computationally":[38],"expensive":[39],"iterative":[40],"learning.":[41,177],"We":[42,79],"propose":[43],"Ridge":[45],"(KRR)":[47],"as":[48,138],"an":[49],"efficient":[50,143],"kernel-based":[51],"alternative":[52],"for":[53,75,145],"high-capacity":[55],"networks.":[57],"KRR":[58,81,95,113,137,168],"utilizes":[59],"the":[60,163,172],"trick":[62],"predicts":[64],"bipolar":[65],"states":[66],"via":[67],"regression,":[68],"crucially":[69],"offering":[70],"a":[71,101,139],"non-iterative,":[72],"closed-form":[73],"solution":[74],"dual":[77],"variables.":[78],"evaluate":[80],"compare":[83],"its":[84],"performance":[85,152],"against":[86],"Hebbian,":[87],"LLR,":[88],"KLR.":[90,111],"Our":[91],"results":[92],"demonstrate":[93],"that":[94],"achieves":[96],"state-of-the-art":[97],"(reaching":[100],"load":[103],"of":[104,120,174],"1.5)":[105],"robustness,":[108],"comparable":[109,151],"to":[110,153],"Crucially,":[112],"drastically":[114],"reduces":[115],"training":[116,157],"time,":[117],"being":[118],"orders":[119],"magnitude":[121],"faster":[122,127],"than":[123,128],"LLR":[124],"KLR,":[129],"especially":[130],"at":[131],"higher":[132],"loads.":[134],"This":[135,160],"establishes":[136],"potent":[140],"highly":[142],"method":[144],"building":[146],"high-performance":[147],"associative":[148],"memories,":[149],"providing":[150],"with":[155],"substantial":[156],"speed":[158],"advantages.":[159],"work":[161],"provides":[162],"first":[164],"empirical":[165],"comparison":[166],"between":[167],"in":[171],"context":[173],"network":[176]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-28T00:00:00"}