{"id":"https://openalex.org/W4309208207","doi":"https://doi.org/10.48550/arxiv.2211.08024","title":"NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction","display_name":"NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction","publication_year":2022,"publication_date":"2022-11-15","ids":{"openalex":"https://openalex.org/W4309208207","doi":"https://doi.org/10.48550/arxiv.2211.08024"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2211.08024","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.08024","pdf_url":"https://arxiv.org/pdf/2211.08024","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2211.08024","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113836837","display_name":"Yun Yi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yi, Yun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018764462","display_name":"Haokui Zhang","orcid":"https://orcid.org/0000-0002-4336-5558"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haokui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101505071","display_name":"Wenze Hu","orcid":"https://orcid.org/0000-0001-5516-7092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Wenze","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690630","display_name":"Nannan Wang","orcid":"https://orcid.org/0000-0003-1435-489X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Nannan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100357613","display_name":"Xiaoyu Wang","orcid":"https://orcid.org/0000-0002-6431-8822"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaoyu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113836837"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9922999739646912,"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":0.9922999739646912,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.991599977016449,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials 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":0.7673510313034058},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6736895442008972},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6345317959785461},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6075976490974426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5655703544616699},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5597319006919861},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5351427793502808},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4931942820549011},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4928600788116455},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4915519654750824},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.47419673204421997},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4670981764793396},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45719990134239197},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08053553104400635}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7673510313034058},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6736895442008972},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6345317959785461},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6075976490974426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5655703544616699},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5597319006919861},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5351427793502808},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4931942820549011},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4928600788116455},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4915519654750824},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.47419673204421997},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4670981764793396},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45719990134239197},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08053553104400635},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2211.08024","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.08024","pdf_url":"https://arxiv.org/pdf/2211.08024","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"doi:10.48550/arxiv.2211.08024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2211.08024","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2211.08024","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2211.08024","pdf_url":"https://arxiv.org/pdf/2211.08024","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1429611257","display_name":null,"funder_award_id":"2018AAA0103202","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G4297946667","display_name":null,"funder_award_id":"U22A2096","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8497982794","display_name":null,"funder_award_id":"6203600","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G913836228","display_name":null,"funder_award_id":"62036007","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4309208207.pdf","grobid_xml":"https://content.openalex.org/works/W4309208207.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2103419012","https://openalex.org/W2988126442","https://openalex.org/W1974414866","https://openalex.org/W2057568687","https://openalex.org/W2063982682","https://openalex.org/W3128807919","https://openalex.org/W3176411177"],"abstract_inverted_index":{"With":[0],"the":[1,22,25,44,50,87,114,170],"wide":[2],"and":[3,20,46,81,89,129,158,173,180,185],"deep":[4,7,182],"adoption":[5],"of":[6,24,37,92,176],"learning":[8],"models":[9,30],"in":[10],"real":[11],"applications,":[12],"there":[13],"is":[14,190],"an":[15,124,132],"increasing":[16],"need":[17],"to":[18,34,70,84,107,168],"model":[19,65,119],"learn":[21],"representations":[23],"neural":[26,39,62,94,183],"networks":[27],"themselves.":[28],"These":[29],"can":[31,67,165],"be":[32,68,166],"used":[33,69,167],"estimate":[35,71],"attributes":[36,73,175],"different":[38],"network":[40,95],"architectures":[41,179],"such":[42],"as":[43],"accuracy":[45,174],"latency,":[47],"without":[48],"running":[49],"actual":[51],"training":[52],"or":[53],"inference":[54],"tasks.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59,76,101,121],"propose":[60,78,123],"a":[61,79,93,97,103,109],"architecture":[63,133],"representation":[64,112],"that":[66,161],"these":[72],"holistically.":[74],"Specifically,":[75],"first":[77],"simple":[80],"effective":[82],"tokenizer":[83],"encode":[85],"both":[86,177],"operation":[88],"topology":[90],"information":[91,125],"into":[96],"single":[98],"sequence.":[99,116],"Then,":[100],"design":[102,131],"multi-stage":[104],"fusion":[105],"transformer":[106],"build":[108],"compact":[110],"vector":[111],"from":[113],"converted":[115],"For":[117],"efficient":[118],"training,":[120],"further":[122],"flow":[126],"consistency":[127,134],"augmentation":[128,142,148],"correspondingly":[130],"loss,":[135],"which":[136],"brings":[137],"more":[138],"benefits":[139],"with":[140,145],"less":[141],"samples":[143],"compared":[144],"previous":[146],"random":[147],"strategies.":[149],"Experiment":[150],"results":[151],"on":[152],"NAS-Bench-101,":[153],"NAS-Bench-201,":[154],"DARTS":[155],"search":[156],"space":[157],"NNLQP":[159],"show":[160],"our":[162],"proposed":[163],"framework":[164],"predict":[169],"aforementioned":[171],"latency":[172],"cell":[178],"whole":[181],"networks,":[184],"achieves":[186],"promising":[187],"performance.":[188],"Code":[189],"available":[191],"at":[192],"https://github.com/yuny220/NAR-Former.":[193]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2022-11-24T00:00:00"}
