{"id":"https://openalex.org/W3109742244","doi":"https://doi.org/10.1109/tpds.2020.3040723","title":"SmartTuning: Selecting Hyper-Parameters of a ConvNet System for Fast Training and Small Working Memory","display_name":"SmartTuning: Selecting Hyper-Parameters of a ConvNet System for Fast Training and Small Working Memory","publication_year":2020,"publication_date":"2020-11-25","ids":{"openalex":"https://openalex.org/W3109742244","doi":"https://doi.org/10.1109/tpds.2020.3040723","mag":"3109742244"},"language":"en","primary_location":{"id":"doi:10.1109/tpds.2020.3040723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpds.2020.3040723","pdf_url":null,"source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Parallel and Distributed Systems","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/A5063860382","display_name":"Xiaqing Li","orcid":"https://orcid.org/0000-0002-7748-7967"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaqing Li","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102854878","display_name":"Guangyan Zhang","orcid":"https://orcid.org/0000-0002-3480-5902"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyan Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3480-5902","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108050911","display_name":"Weimin Zheng","orcid":"https://orcid.org/0000-0002-4450-5428"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weimin Zheng","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.2875,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.57517277,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"32","issue":"7","first_page":"1690","last_page":"1701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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":0.9947999715805054,"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/computer-science","display_name":"Computer science","score":0.8757760524749756},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.8375257849693298},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8167319297790527},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6515252590179443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6450159549713135},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5360404253005981},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4797981083393097},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4747864603996277},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4391149580478668},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4278544783592224},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42156392335891724},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.30359047651290894},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2793993651866913}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8757760524749756},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8375257849693298},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8167319297790527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6515252590179443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6450159549713135},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5360404253005981},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4797981083393097},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4747864603996277},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4391149580478668},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4278544783592224},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42156392335891724},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30359047651290894},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2793993651866913},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tpds.2020.3040723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpds.2020.3040723","pdf_url":null,"source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Parallel and Distributed Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5099999904632568}],"awards":[{"id":"https://openalex.org/G6518094076","display_name":"\u8d44\u6e90\u5168\u5c40\u5171\u4eab\u7684\u5b58\u50a8\u9635\u5217\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61672315","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":104,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W135104305","https://openalex.org/W1536680647","https://openalex.org/W1569788011","https://openalex.org/W1665719966","https://openalex.org/W1667652561","https://openalex.org/W1769599646","https://openalex.org/W1789336918","https://openalex.org/W1798702550","https://openalex.org/W1925579137","https://openalex.org/W1935978687","https://openalex.org/W2062118960","https://openalex.org/W2097998348","https://openalex.org/W2106411961","https://openalex.org/W2131241448","https://openalex.org/W2137135057","https://openalex.org/W2138857742","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2175583736","https://openalex.org/W2233116163","https://openalex.org/W2409689189","https://openalex.org/W2427941034","https://openalex.org/W2510697685","https://openalex.org/W2521727659","https://openalex.org/W2556372419","https://openalex.org/W2556522401","https://openalex.org/W2565639579","https://openalex.org/W2598465958","https://openalex.org/W2612445135","https://openalex.org/W2613868844","https://openalex.org/W2616619952","https://openalex.org/W2732547613","https://openalex.org/W2751836095","https://openalex.org/W2761434131","https://openalex.org/W2772089072","https://openalex.org/W2788853733","https://openalex.org/W2885661469","https://openalex.org/W2885833634","https://openalex.org/W2903933688","https://openalex.org/W2915992092","https://openalex.org/W2950277768","https://openalex.org/W2950646495","https://openalex.org/W2951216772","https://openalex.org/W2951665052","https://openalex.org/W2951810231","https://openalex.org/W2952519754","https://openalex.org/W2953384591","https://openalex.org/W2953807672","https://openalex.org/W2954658179","https://openalex.org/W2962202409","https://openalex.org/W2962750597","https://openalex.org/W2963150697","https://openalex.org/W2963423218","https://openalex.org/W2963542991","https://openalex.org/W2963642335","https://openalex.org/W2963742654","https://openalex.org/W2963815651","https://openalex.org/W2963981420","https://openalex.org/W2964136676","https://openalex.org/W2964515685","https://openalex.org/W2967733054","https://openalex.org/W2981985696","https://openalex.org/W2988371863","https://openalex.org/W2995727387","https://openalex.org/W3012573144","https://openalex.org/W3022247380","https://openalex.org/W3102150134","https://openalex.org/W3124229194","https://openalex.org/W4250482878","https://openalex.org/W4289763996","https://openalex.org/W4293870975","https://openalex.org/W4297775537","https://openalex.org/W4298052809","https://openalex.org/W4302296459","https://openalex.org/W6629368666","https://openalex.org/W6634168845","https://openalex.org/W6637151318","https://openalex.org/W6638020065","https://openalex.org/W6638209102","https://openalex.org/W6640289440","https://openalex.org/W6674385629","https://openalex.org/W6676179485","https://openalex.org/W6678911119","https://openalex.org/W6680281042","https://openalex.org/W6680300913","https://openalex.org/W6681821898","https://openalex.org/W6684191040","https://openalex.org/W6685594460","https://openalex.org/W6713134421","https://openalex.org/W6717592245","https://openalex.org/W6724998850","https://openalex.org/W6729429149","https://openalex.org/W6730169791","https://openalex.org/W6730269975","https://openalex.org/W6734985462","https://openalex.org/W6737664043","https://openalex.org/W6737809505","https://openalex.org/W6743745977","https://openalex.org/W6748587240","https://openalex.org/W6751421292","https://openalex.org/W6753331991","https://openalex.org/W6759828284","https://openalex.org/W6764988152"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W3102660566","https://openalex.org/W2097707447","https://openalex.org/W2372267530","https://openalex.org/W2969189870","https://openalex.org/W3015855446","https://openalex.org/W4303857162","https://openalex.org/W2965643117"],"abstract_inverted_index":{"It":[0],"is":[1,90],"desirable":[2],"to":[3,24,62,91,102,106],"deploy":[4],"a":[5,51,67,93,98,118,146],"ConvNet":[6,52,68,99,119,147],"system":[7,69],"with":[8,78,165,192],"high":[9,30,34,71,82],"inference":[10,19,83,127,132,180],"accuracy,":[11,35,128],"as":[12,14],"well":[13],"fast":[15],"training":[16,72,129,167],"and":[17,44,74,101,114,131,139,173,177,187],"small":[18,75],"memory.":[20],"However,":[21],"existing":[22,193],"approaches":[23],"hyper-parameter":[25,158],"tuning":[26,36,137,194],"only":[27],"focus":[28],"on":[29],"accuracy.":[31,84],"Although":[32],"achieving":[33],"poorly":[37],"can":[38,125,154],"significantly":[39],"increase":[40],"the":[41,47,64,79,108,111,115,136,142,157],"performance":[42,49,95,113,144],"burden,":[43],"thus":[45,140],"degrade":[46],"overall":[48,112,143],"of":[50,66,81,88,117,145],"system.":[53,120,148],"In":[54,121],"this":[55,122],"article,":[56],"we":[57],"propose":[58],"SmartTuning,":[59],"an":[60],"approach":[61],"identifying":[63],"hyper-parameters":[65,116],"for":[70,97],"speed":[73,130,168],"working":[76],"memory,":[77],"restriction":[80],"The":[85],"key":[86],"idea":[87],"SmartTuning":[89,124,153],"build":[92],"new":[94],"model":[96],"system,":[100],"integrate":[103],"Bayesian":[104],"Optimization":[105],"learn":[107],"relationship":[109],"between":[110],"way,":[123],"balance":[126],"memory":[133,181],"usage":[134,182],"during":[135],"process,":[138],"maximizes":[141],"Our":[149],"experiments":[150],"show":[151],"that":[152,160],"stably":[155],"identify":[156],"sets":[159],"offer":[161],"very":[162],"close":[163],"accuracy":[164],"faster":[166],"(i.e.,":[169,183],"7\u00d7-11\u00d7":[170],"over":[171,175,185,189],"MNIST":[172,186],"2\u00d7-3\u00d7":[174],"CIFAR-10)":[176],"much":[178],"less":[179],"17\u00d7-23\u00d7":[184],"4\u00d7-9\u00d7":[188],"CIFAR-10),":[190],"compared":[191],"approaches.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
