{"id":"https://openalex.org/W2617308160","doi":"https://doi.org/10.1147/jrd.2017.2709578","title":"An effective algorithm for hyperparameter optimization of neural networks","display_name":"An effective algorithm for hyperparameter optimization of neural networks","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2617308160","doi":"https://doi.org/10.1147/jrd.2017.2709578","mag":"2617308160"},"language":"en","primary_location":{"id":"doi:10.1147/jrd.2017.2709578","is_oa":false,"landing_page_url":"https://doi.org/10.1147/jrd.2017.2709578","pdf_url":null,"source":{"id":"https://openalex.org/S4210219925","display_name":"IBM Journal of Research and Development","issn_l":"0018-8646","issn":["0018-8646","2151-8556"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320652","host_organization_name":"IBM","host_organization_lineage":["https://openalex.org/P4310320652"],"host_organization_lineage_names":["IBM"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IBM Journal of Research and Development","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1705.08520","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015082669","display_name":"Gonzalo I. Diaz","orcid":"https://orcid.org/0000-0001-6937-7904"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G. I. Diaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062643837","display_name":"Achille Fokoue","orcid":"https://orcid.org/0000-0003-1137-1344"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"A. Fokoue-Nkoutche","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005485061","display_name":"Giacomo Nannicini","orcid":"https://orcid.org/0000-0002-4936-1259"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G. Nannicini","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035277014","display_name":"Horst Samulowitz","orcid":"https://orcid.org/0000-0002-6780-3217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H. Samulowitz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1505,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.84247445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"61","issue":"4/5","first_page":"9:1","last_page":"9:11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9972000122070312,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/computer-science","display_name":"Computer science","score":0.7426846027374268},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7422171831130981},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.667151689529419},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6286797523498535},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6225489377975464},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5085204839706421},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4980947971343994},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.48690879344940186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47218266129493713},{"id":"https://openalex.org/keywords/parametrization","display_name":"Parametrization (atmospheric modeling)","score":0.447734534740448},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.43343642354011536},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4097588062286377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3757934868335724},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1311984360218048}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7426846027374268},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7422171831130981},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.667151689529419},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6286797523498535},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6225489377975464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5085204839706421},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4980947971343994},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.48690879344940186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47218266129493713},{"id":"https://openalex.org/C202887219","wikidata":"https://www.wikidata.org/wiki/Q3895221","display_name":"Parametrization (atmospheric modeling)","level":3,"score":0.447734534740448},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.43343642354011536},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4097588062286377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3757934868335724},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1311984360218048},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C74902906","wikidata":"https://www.wikidata.org/wiki/Q1190858","display_name":"Radiative transfer","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1147/jrd.2017.2709578","is_oa":false,"landing_page_url":"https://doi.org/10.1147/jrd.2017.2709578","pdf_url":null,"source":{"id":"https://openalex.org/S4210219925","display_name":"IBM Journal of Research and Development","issn_l":"0018-8646","issn":["0018-8646","2151-8556"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320652","host_organization_name":"IBM","host_organization_lineage":["https://openalex.org/P4310320652"],"host_organization_lineage_names":["IBM"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IBM Journal of Research and Development","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1705.08520","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1705.08520","pdf_url":"https://arxiv.org/pdf/1705.08520","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2617308160","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/1705.08520.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1705.08520","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1705.08520","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1705.08520","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1705.08520","pdf_url":"https://arxiv.org/pdf/1705.08520","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320308943","display_name":"Microsoft Research","ror":"https://ror.org/00d0nc645"},{"id":"https://openalex.org/F4320322015","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087"},{"id":"https://openalex.org/F4320324232","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34"},{"id":"https://openalex.org/F4320336061","display_name":"Research England","ror":"https://ror.org/02wxr8x18"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2617308160.pdf","grobid_xml":"https://content.openalex.org/works/W2617308160.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W181733065","https://openalex.org/W1529533208","https://openalex.org/W1548885290","https://openalex.org/W1573878755","https://openalex.org/W1975046772","https://openalex.org/W1988945200","https://openalex.org/W2097998348","https://openalex.org/W2102539288","https://openalex.org/W2127795553","https://openalex.org/W2200000192","https://openalex.org/W2266445208","https://openalex.org/W2266822037","https://openalex.org/W2295611744","https://openalex.org/W2309832917","https://openalex.org/W2497965792","https://openalex.org/W2587014634","https://openalex.org/W2950182411","https://openalex.org/W6632708280","https://openalex.org/W6636966703","https://openalex.org/W6674385629","https://openalex.org/W6676179485","https://openalex.org/W6678830454","https://openalex.org/W6678911119","https://openalex.org/W6685961532","https://openalex.org/W6687744914","https://openalex.org/W6693270258","https://openalex.org/W6697084357","https://openalex.org/W6713783767","https://openalex.org/W6733065090"],"related_works":["https://openalex.org/W2964306294","https://openalex.org/W3191950921","https://openalex.org/W1606456968","https://openalex.org/W3043378470","https://openalex.org/W3120911073","https://openalex.org/W3035825996","https://openalex.org/W1501729618","https://openalex.org/W2899462220","https://openalex.org/W3012926379","https://openalex.org/W3034172904","https://openalex.org/W120971688","https://openalex.org/W2766674581","https://openalex.org/W1631708156","https://openalex.org/W2810206120","https://openalex.org/W2808236801","https://openalex.org/W2787343346","https://openalex.org/W3122649490","https://openalex.org/W2761730264","https://openalex.org/W3124600377","https://openalex.org/W186881209"],"abstract_inverted_index":{"A":[0],"major":[1],"challenge":[2],"in":[3,180,194],"designing":[4],"neural":[5],"network":[6,19],"(NN)":[7],"systems":[8],"is":[9,174,197],"to":[10,136,153],"determine":[11],"the":[12,18,21,24,34,40,44,67,74,85,92,114,127,134,138,149,161,171,181],"best":[13],"structure":[14],"and":[15,38,43,56,103,111,159,179],"parameters":[16,32,49,90],"for":[17,23,91],"given":[20],"data":[22],"machine":[25],"learning":[26,41],"problem":[27,86,102],"at":[28],"hand.":[29],"Examples":[30],"of":[31,36,69,73,87,126,133,140,157,170,183],"are":[33,50,57,151],"number":[35,156],"layers":[37],"nodes,":[39],"rates,":[42],"dropout":[45],"rates.":[46],"Typically,":[47],"these":[48],"chosen":[51],"based":[52],"on":[53,176],"heuristic":[54],"rules":[55],"manually":[58],"fine-tuned,":[59],"which":[60],"may":[61,76],"be":[62],"very":[63],"time-consuming,":[64],"because":[65],"evaluating":[66],"performance":[68,169],"a":[70,98,105,121,154],"single":[71],"parametrization":[72],"NN":[75,93],"require":[77],"several":[78],"hours.":[79],"In":[80],"this":[81,195],"paper,":[82],"we":[83],"address":[84],"choosing":[88],"appropriate":[89],"by":[94,148],"formulating":[95],"it":[96],"as":[97],"box-constrained":[99],"mathematical":[100],"optimization":[101,107,118,191],"applying":[104],"derivative-free":[106],"tool":[108,119,192],"that":[109],"automatically":[110],"effectively":[112],"searches":[113],"parameter":[115],"space.":[116],"The":[117,168,190],"employs":[120],"radial":[122],"basis":[123],"function":[124,129],"model":[125],"objective":[128],"(the":[130],"prediction":[131],"accuracy":[132],"NN)":[135],"accelerate":[137],"discovery":[139],"configurations":[141,146],"yielding":[142],"high":[143],"accuracy.":[144],"Candidate":[145],"explored":[147],"algorithm":[150],"trained":[152],"small":[155],"epochs,":[158],"only":[160],"most":[162],"promising":[163,188],"candidates":[164],"receive":[165],"full":[166],"training.":[167],"proposed":[172],"methodology":[173],"assessed":[175],"benchmark":[177],"sets":[178],"context":[182],"predicting":[184],"drug\u2013drug":[185],"interactions,":[186],"showing":[187],"results.":[189],"used":[193],"paper":[196],"open":[198],"source.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
