{"id":"https://openalex.org/W3090956725","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207600","title":"NASABN: A Neural Architecture Search Framework for Attention-Based Networks","display_name":"NASABN: A Neural Architecture Search Framework for Attention-Based Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090956725","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207600","mag":"3090956725"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5049652197","display_name":"Kun Jing","orcid":"https://orcid.org/0000-0001-9028-8869"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Jing","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016973102","display_name":"Jungang Xu","orcid":"https://orcid.org/0000-0002-3994-1401"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jungang Xu","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036631313","display_name":"Hui Xu Zugeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui Xu Zugeng","raw_affiliation_strings":["Zugeng Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zugeng Technology, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2563,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.57144993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994000196456909,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9983999729156494,"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":0.8146075010299683},{"id":"https://openalex.org/keywords/treebank","display_name":"Treebank","score":0.7704362869262695},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6844415664672852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.657334566116333},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6417006254196167},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5668225884437561},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5577460527420044},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5485509037971497},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5093532800674438},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49416878819465637},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08219432830810547}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8146075010299683},{"id":"https://openalex.org/C206134035","wikidata":"https://www.wikidata.org/wiki/Q811525","display_name":"Treebank","level":3,"score":0.7704362869262695},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6844415664672852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.657334566116333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6417006254196167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5668225884437561},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5577460527420044},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5485509037971497},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5093532800674438},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49416878819465637},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08219432830810547},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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":102,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W1514535095","https://openalex.org/W1632114991","https://openalex.org/W1868018859","https://openalex.org/W1902237438","https://openalex.org/W1994197834","https://openalex.org/W2097998348","https://openalex.org/W2109943925","https://openalex.org/W2113207845","https://openalex.org/W2131241448","https://openalex.org/W2132196015","https://openalex.org/W2133564696","https://openalex.org/W2194775991","https://openalex.org/W2270245739","https://openalex.org/W2418011751","https://openalex.org/W2507756961","https://openalex.org/W2549416390","https://openalex.org/W2553303224","https://openalex.org/W2556833785","https://openalex.org/W2558263348","https://openalex.org/W2594529350","https://openalex.org/W2757338536","https://openalex.org/W2782417188","https://openalex.org/W2796265726","https://openalex.org/W2810075754","https://openalex.org/W2818275232","https://openalex.org/W2888429796","https://openalex.org/W2902994895","https://openalex.org/W2919115771","https://openalex.org/W2921286775","https://openalex.org/W2924820442","https://openalex.org/W2949264490","https://openalex.org/W2951104886","https://openalex.org/W2960010704","https://openalex.org/W2962746461","https://openalex.org/W2962832505","https://openalex.org/W2962847160","https://openalex.org/W2962964385","https://openalex.org/W2963077125","https://openalex.org/W2963233958","https://openalex.org/W2963374479","https://openalex.org/W2963403868","https://openalex.org/W2963423218","https://openalex.org/W2963446712","https://openalex.org/W2963474950","https://openalex.org/W2963494889","https://openalex.org/W2963537482","https://openalex.org/W2963748792","https://openalex.org/W2963778169","https://openalex.org/W2963821229","https://openalex.org/W2963983719","https://openalex.org/W2964081403","https://openalex.org/W2964081807","https://openalex.org/W2964308564","https://openalex.org/W2964331719","https://openalex.org/W2965658867","https://openalex.org/W4255158661","https://openalex.org/W4288409786","https://openalex.org/W4289763996","https://openalex.org/W4294555862","https://openalex.org/W4295185264","https://openalex.org/W4298422451","https://openalex.org/W4299838440","https://openalex.org/W4300427683","https://openalex.org/W4300687381","https://openalex.org/W4300687870","https://openalex.org/W4385245566","https://openalex.org/W6630875275","https://openalex.org/W6636649193","https://openalex.org/W6639506587","https://openalex.org/W6674385629","https://openalex.org/W6676404980","https://openalex.org/W6677088747","https://openalex.org/W6678911119","https://openalex.org/W6679434410","https://openalex.org/W6693919493","https://openalex.org/W6713783767","https://openalex.org/W6716843620","https://openalex.org/W6720905350","https://openalex.org/W6725207838","https://openalex.org/W6727099177","https://openalex.org/W6729239390","https://openalex.org/W6729783475","https://openalex.org/W6729956949","https://openalex.org/W6729972426","https://openalex.org/W6734593296","https://openalex.org/W6739901393","https://openalex.org/W6741459021","https://openalex.org/W6742632731","https://openalex.org/W6744574350","https://openalex.org/W6745265922","https://openalex.org/W6745614327","https://openalex.org/W6746582238","https://openalex.org/W6748057086","https://openalex.org/W6751421292","https://openalex.org/W6752515464","https://openalex.org/W6753303928","https://openalex.org/W6753701838","https://openalex.org/W6756610502","https://openalex.org/W6757204547","https://openalex.org/W6760274136","https://openalex.org/W6761154904"],"related_works":["https://openalex.org/W2740662036","https://openalex.org/W3142119062","https://openalex.org/W3150294986","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067","https://openalex.org/W2979433843","https://openalex.org/W3208304128"],"abstract_inverted_index":{"Recently,":[0],"neural":[1,25,64],"architecture":[2,49,65],"search":[3,47,66],"(NAS)":[4],"has":[5],"emerged":[6],"as":[7,22],"a":[8,61],"technique":[9],"of":[10,50,79],"growing":[11],"concern":[12],"in":[13,34],"automatic":[14],"machine":[15],"learning":[16,36],"(AutoML).":[17],"Meanwhile,":[18],"attention-based":[19,23,51,68,73],"models,":[20],"such":[21],"recurrent":[24],"network,":[26],"transformer-based":[27],"model,":[28,81],"etc.,":[29],"have":[30],"been":[31],"widely":[32],"used":[33],"deep":[35],"applications.":[37],"However,":[38],"there":[39],"is":[40,89],"no":[41],"efficient":[42],"NAS":[43,97],"method":[44],"that":[45],"can":[46,100],"the":[48,80,83,134],"model":[52],"so":[53],"far.":[54],"To":[55],"solve":[56],"this":[57],"problem,":[58],"we":[59],"propose":[60],"framework":[62],"named":[63],"for":[67],"networks":[69],"(NASABN)":[70],"by":[71],"abstracting":[72],"models":[74],"and":[75,86,91,123,128],"extracting":[76],"undefined":[77],"parts":[78],"including":[82],"attention":[84],"layers":[85],"cells.":[87],"NASABN":[88,112],"flexible":[90],"general":[92],"enough":[93],"to":[94],"fit":[95],"different":[96,105],"methods,":[98],"which":[99],"also":[101],"be":[102],"transferred":[103],"across":[104],"datasets.":[106],"We":[107],"conduct":[108],"extensive":[109],"experiments":[110],"with":[111,133],"using":[113],"gradient":[114],"descent-based":[115],"methods":[116],"like":[117],"DARTS":[118],"on":[119],"Penn":[120],"Treebank":[121],"(PTB)":[122],"WikiText-2":[124],"(WT2)":[125],"datasets":[126],"respectively,":[127],"achieve":[129],"competitive":[130],"performance":[131],"compared":[132],"state-of-the-art":[135],"methods.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}