{"id":"https://openalex.org/W4410398301","doi":"https://doi.org/10.32473/flairs.38.1.138986","title":"Curvature-Adaptive Learning Rate Optimizer: Theoretical Insights and Empirical Evaluation on Neural Network Training","display_name":"Curvature-Adaptive Learning Rate Optimizer: Theoretical Insights and Empirical Evaluation on Neural Network Training","publication_year":2025,"publication_date":"2025-05-14","ids":{"openalex":"https://openalex.org/W4410398301","doi":"https://doi.org/10.32473/flairs.38.1.138986"},"language":"en","primary_location":{"id":"doi:10.32473/flairs.38.1.138986","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.138986","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/138986/144070","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journals.flvc.org/FLAIRS/article/download/138986/144070","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056789912","display_name":"Kehelwala Dewage Gayan Maduranga","orcid":"https://orcid.org/0000-0002-6626-9024"},"institutions":[{"id":"https://openalex.org/I63920570","display_name":"Tennessee Technological University","ror":"https://ror.org/05drmrq39","country_code":"US","type":"education","lineage":["https://openalex.org/I63920570"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kehelwala Dewage Gayan Maduranga","raw_affiliation_strings":["Tennessee Technological University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tennessee Technological University","institution_ids":["https://openalex.org/I63920570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5056789912"],"corresponding_institution_ids":["https://openalex.org/I63920570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16272189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.744700014591217,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T14510","display_name":"Medical Imaging and Analysis","score":0.744700014591217,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7321252822875977},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6801043748855591},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5553033351898193},{"id":"https://openalex.org/keywords/curvature","display_name":"Curvature","score":0.528819739818573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45608019828796387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3725738525390625},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17482319474220276},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0676514208316803}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7321252822875977},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6801043748855591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5553033351898193},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.528819739818573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45608019828796387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3725738525390625},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17482319474220276},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0676514208316803},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.32473/flairs.38.1.138986","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.138986","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/138986/144070","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b46219a33eba42a6ab3616bd40caa422","is_oa":true,"landing_page_url":"https://doaj.org/article/b46219a33eba42a6ab3616bd40caa422","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the International Florida Artificial Intelligence Research Society Conference, Vol 38, Iss 1 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.32473/flairs.38.1.138986","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.138986","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/138986/144070","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5385510318","display_name":null,"funder_award_id":"2127188","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410398301.pdf","grobid_xml":"https://content.openalex.org/works/W4410398301.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1815076433","https://openalex.org/W1988720110","https://openalex.org/W2026258334","https://openalex.org/W2040870580","https://openalex.org/W2112796928","https://openalex.org/W2146502635","https://openalex.org/W2163605009","https://openalex.org/W6600490550","https://openalex.org/W6608133726","https://openalex.org/W6631190155","https://openalex.org/W6642904842","https://openalex.org/W6648914341","https://openalex.org/W6739901393","https://openalex.org/W6757107679","https://openalex.org/W6842097421"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Optimizing":[0],"neural":[1,100,132],"networks":[2],"often":[3],"encounters":[4],"challenges":[5],"such":[6],"as":[7,125],"saddle":[8],"points,":[9],"plateaus,":[10],"and":[11,23,58,65,85,106,114],"ill-conditioned":[12],"curvature,":[13],"limiting":[14],"the":[15,31],"effectiveness":[16],"of":[17],"standard":[18],"optimizers":[19],"like":[20],"Adam,":[21],"Nadam,":[22],"RMSProp.":[24],"To":[25],"address":[26],"these":[27],"limitations,":[28],"we":[29],"propose":[30],"Curvature-Adaptive":[32],"Learning":[33],"Rate":[34],"(CALR)":[35],"optimizer,":[36],"a":[37,126],"novel":[38],"method":[39],"that":[40],"leverages":[41],"local":[42],"curvature":[43],"estimates":[44],"to":[45,118],"dynamically":[46],"adjust":[47],"learning":[48],"rates.":[49],"CALR,":[50],"along":[51],"with":[52],"its":[53,88],"variants":[54],"incorporating":[55],"gradient":[56],"clipping":[57],"cosine":[59],"annealing":[60],"schedules,":[61],"offers":[62],"enhanced":[63],"robustness":[64],"faster":[66,110],"convergence":[67,76],"across":[68],"diverse":[69],"optimization":[70,92,128],"tasks.":[71],"Theoretical":[72],"analysis":[73],"confirms":[74],"CALR\u2019s":[75],"properties,":[77],"while":[78],"empirical":[79],"evaluations":[80],"on":[81,99],"benchmark":[82],"functions\u2014Rosenbrock,":[83],"Himmelblau,":[84],"Saddle":[86],"Point\u2014highlight":[87],"efficiency":[89],"in":[90],"complex":[91],"landscapes.":[93],"Furthermore,":[94],"CALR":[95,124],"demonstrates":[96],"superior":[97],"performance":[98],"network":[101,133],"training":[102,134],"tasks":[103],"using":[104],"MNIST":[105],"CIFAR-10":[107],"datasets,":[108],"achieving":[109],"convergence,":[111],"lower":[112],"loss,":[113],"better":[115],"generalization":[116],"compared":[117],"traditional":[119],"optimizers.":[120],"These":[121],"results":[122],"establish":[123],"promising":[127],"strategy":[129],"for":[130],"challenging":[131],"problems.":[135]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}