{"id":"https://openalex.org/W4210647952","doi":"https://doi.org/10.1145/3506695","title":"An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks","display_name":"An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks","publication_year":2022,"publication_date":"2022-01-31","ids":{"openalex":"https://openalex.org/W4210647952","doi":"https://doi.org/10.1145/3506695"},"language":"en","primary_location":{"id":"doi:10.1145/3506695","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3506695","pdf_url":null,"source":{"id":"https://openalex.org/S142627899","display_name":"ACM Transactions on Software Engineering and Methodology","issn_l":"1049-331X","issn":["1049-331X","1557-7392"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Software Engineering and Methodology","raw_type":"journal-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/A5103209936","display_name":"Lizhi Liao","orcid":"https://orcid.org/0000-0001-9920-5855"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Lizhi Liao","raw_affiliation_strings":["Concordia University, Montr\u00e9al, Qu\u00e9bec, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9920-5855","affiliations":[{"raw_affiliation_string":"Concordia University, Montr\u00e9al, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338781","display_name":"Heng Li","orcid":"https://orcid.org/0000-0001-5441-6763"},"institutions":[{"id":"https://openalex.org/I45683168","display_name":"Polytechnique Montr\u00e9al","ror":"https://ror.org/05f8d4e86","country_code":"CA","type":"education","lineage":["https://openalex.org/I45683168"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Heng Li","raw_affiliation_strings":["Polytechnique Montr\u00e9al, Montr\u00e9al, Qu\u00e9bec, Canada"],"raw_orcid":"https://orcid.org/0000-0001-5441-6763","affiliations":[{"raw_affiliation_string":"Polytechnique Montr\u00e9al, Montr\u00e9al, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I45683168"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056378414","display_name":"Weiyi Shang","orcid":"https://orcid.org/0000-0001-6222-7444"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Weiyi Shang","raw_affiliation_strings":["Concordia University, Montr\u00e9al, Qu\u00e9bec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University, Montr\u00e9al, Qu\u00e9bec, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101468661","display_name":"Lei Ma","orcid":"https://orcid.org/0000-0002-8621-2420"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Lei Ma","raw_affiliation_strings":["University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.6055,"has_fulltext":false,"cited_by_count":201,"citation_normalized_percentile":{"value":0.99469274,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"31","issue":"3","first_page":"1","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.994700014591217,"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/hyperparameter","display_name":"Hyperparameter","score":0.9034918546676636},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8457967042922974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6143779754638672},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5590577721595764},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5381032228469849},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5230406522750854},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.4919099807739258},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.41813552379608154},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4166862964630127},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13830417394638062}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9034918546676636},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8457967042922974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6143779754638672},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5590577721595764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5381032228469849},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5230406522750854},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.4919099807739258},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.41813552379608154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4166862964630127},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13830417394638062},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3506695","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3506695","pdf_url":null,"source":{"id":"https://openalex.org/S142627899","display_name":"ACM Transactions on Software Engineering and Methodology","issn_l":"1049-331X","issn":["1049-331X","1557-7392"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Software Engineering and Methodology","raw_type":"journal-article"},{"id":"pmh:oai:publications.polymtl.ca:51117","is_oa":false,"landing_page_url":"https://publications.polymtl.ca/51117/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401013","display_name":"PolyPublie (\u00c9cole Polytechnique de Montr\u00e9al)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45683168","host_organization_name":"Polytechnique Montr\u00e9al","host_organization_lineage":["https://openalex.org/I45683168"],"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":"Article de revue"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W643078925","https://openalex.org/W1544601482","https://openalex.org/W1765613701","https://openalex.org/W1845051632","https://openalex.org/W1902482618","https://openalex.org/W2012322522","https://openalex.org/W2029320154","https://openalex.org/W2095705004","https://openalex.org/W2102539288","https://openalex.org/W2111526171","https://openalex.org/W2119144962","https://openalex.org/W2143381319","https://openalex.org/W2156267707","https://openalex.org/W2177847924","https://openalex.org/W2179337826","https://openalex.org/W2276892413","https://openalex.org/W2386192529","https://openalex.org/W2419597278","https://openalex.org/W2476791860","https://openalex.org/W2541249978","https://openalex.org/W2546913659","https://openalex.org/W2562607067","https://openalex.org/W2604272474","https://openalex.org/W2604319603","https://openalex.org/W2608595939","https://openalex.org/W2611669764","https://openalex.org/W2616028256","https://openalex.org/W2618530766","https://openalex.org/W2707890836","https://openalex.org/W2737382101","https://openalex.org/W2738180183","https://openalex.org/W2766839578","https://openalex.org/W2770687010","https://openalex.org/W2788653909","https://openalex.org/W2799197246","https://openalex.org/W2803354197","https://openalex.org/W2806331304","https://openalex.org/W2808168148","https://openalex.org/W2810271953","https://openalex.org/W2893813411","https://openalex.org/W2902251695","https://openalex.org/W2902736485","https://openalex.org/W2913389484","https://openalex.org/W2925035065","https://openalex.org/W2937888061","https://openalex.org/W2962965870","https://openalex.org/W2962988160","https://openalex.org/W2963674932","https://openalex.org/W2963918968","https://openalex.org/W2963945905","https://openalex.org/W2964024268","https://openalex.org/W2964259004","https://openalex.org/W2972198316","https://openalex.org/W2972268045","https://openalex.org/W2976174503","https://openalex.org/W2981052417","https://openalex.org/W2984356918","https://openalex.org/W2995694143","https://openalex.org/W2998218113","https://openalex.org/W2998993395","https://openalex.org/W2999552082","https://openalex.org/W3008725350","https://openalex.org/W3032945613","https://openalex.org/W3035078899","https://openalex.org/W3047398590","https://openalex.org/W3082803092","https://openalex.org/W3100440193","https://openalex.org/W3108436559","https://openalex.org/W3109046287","https://openalex.org/W4288409632","https://openalex.org/W4289305285","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6726275242"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4205712847","https://openalex.org/W4281646320","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4287818966","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"network":[2],"(DNN)":[3],"models":[4,29,36,103,147,177,289,292,304,353],"typically":[5],"have":[6,306,312],"many":[7],"hyperparameters":[8,25,239,280,338,348],"that":[9,49,153,277,327,377],"can":[10,379],"be":[11],"configured":[12],"to":[13,43,144,233,384],"achieve":[14],"optimal":[15,46],"performance":[16,85,173,265,286,295,315,356,359,388],"on":[17,65,210,284,334,339,370],"a":[18,32,207,331,385],"particular":[19],"dataset.":[20],"Practitioners":[21],"usually":[22,63,305],"tune":[23],"the":[24,41,45,51,56,66,70,81,98,167,187,192,198,241,249,257,285,300,335,371,391],"of":[26,34,40,87,126,175,194,263,287,318,337,347,387,394],"their":[27,358,401],"DNN":[28,88,102,146,176,215,237,243,259,288,303,340,402],"by":[30],"training":[31,52,57],"number":[33],"trial":[35],"with":[37,253,354],"different":[38,236,291,294,314,363],"configurations":[39],"hyperparameters,":[42],"find":[44],"hyperparameter":[47,61,195,250],"configuration":[48],"maximizes":[50],"accuracy":[53,68],"or":[54,69,140,270],"minimizes":[55],"loss.":[58],"As":[59],"such":[60,90,120,178],"tuning":[62,196,251,278,398],"focuses":[64],"model":[67,95,135,168,188,238,254,328,341,395],"loss":[71],"function,":[72],"it":[73,162],"is":[74,163],"not":[75],"clear":[76,165],"and":[77,94,109,124,149,182,213,228,293,368,390,399],"remains":[78],"under-explored":[79],"how":[80,166,186,235,248],"process":[82],"impacts":[83],"other":[84,99,172,319],"properties":[86,174,266,389],"models,":[89,216,244,260],"as":[91,121,179,245,247],"inference":[92,180,268,322],"latency":[93,181,269],"size.":[96],"On":[97],"hand,":[100],"standard":[101,242,352],"are":[104,142,155,366],"often":[105],"large":[106],"in":[107,117,202,261,316,351],"size":[108],"computing-intensive,":[110],"prohibiting":[111],"them":[112],"from":[113,381],"being":[114],"directly":[115],"deployed":[116,369],"resource-bounded":[118,159],"environments":[119],"mobile":[122,372],"devices":[123],"Internet":[125],"Things":[127],"(IoT)":[128],"devices.":[129],"To":[130],"tackle":[131],"this":[132,203],"problem,":[133],"various":[134,264],"optimization":[136,169,189,255,329,396],"techniques":[137,170,190],"(e.g.,":[138,267,321],"pruning":[139],"quantization)":[141],"proposed":[143],"make":[145],"smaller":[148],"less":[150],"computing-intensive":[151],"so":[152],"they":[154,310,365],"better":[156],"suited":[157],"for":[158],"environments.":[160],"However,":[161],"neither":[164],"impact":[171,191,283,336],"battery":[183,271],"consumption,":[184],"nor":[185],"effect":[193,333,393],"(i.e.,":[197],"compounding":[199],"effect).":[200],"Therefore,":[201],"paper,":[204],"we":[205],"perform":[206],"comprehensive":[208],"study":[209],"four":[211],"representative":[212],"widely-adopted":[214],"i.e.,":[217],"CNN":[218,224],"image":[219],"classification":[220,226,231],",":[221,223,227,232],"Resnet-50":[222],"text":[225],"LSTM":[229],"sentiment":[230],"investigate":[234],"affect":[240,256],"well":[246],"combined":[252],"optimized":[258,367],"terms":[262,317],"consumption).":[272],"Our":[273,374],"empirical":[274],"results":[275],"indicate":[276],"specific":[279],"has":[281,330],"heterogeneous":[282],"across":[290],"properties.":[296],"In":[297],"particular,":[298],"although":[299],"top":[301],"tuned":[302],"very":[307],"similar":[308,355],"accuracy,":[309],"may":[311,349,360],"significantly":[313,362],"aspects":[320],"latency).":[323],"We":[324],"also":[325],"observe":[326],"confounding":[332,392],"performance.":[342],"For":[343],"example,":[344],"two":[345],"sets":[346],"result":[350],"but":[357],"become":[361],"after":[364],"device.":[373],"findings":[375],"highlight":[376],"practitioners":[378],"benefit":[380],"paying":[382],"attention":[383],"variety":[386],"when":[397],"optimizing":[400],"models.":[403]},"counts_by_year":[{"year":2026,"cited_by_count":33},{"year":2025,"cited_by_count":90},{"year":2024,"cited_by_count":54},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2025-10-10T00:00:00"}