{"id":"https://openalex.org/W4399910982","doi":"https://doi.org/10.1145/3653946.3653963","title":"DDS-NAS-Bench: Towards predictors under Data Distribution Shift","display_name":"DDS-NAS-Bench: Towards predictors under Data Distribution Shift","publication_year":2024,"publication_date":"2024-03-12","ids":{"openalex":"https://openalex.org/W4399910982","doi":"https://doi.org/10.1145/3653946.3653963"},"language":"en","primary_location":{"id":"doi:10.1145/3653946.3653963","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3653946.3653963","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3653946.3653963","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Machine Vision and Applications","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3653946.3653963","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015329464","display_name":"Sofia Casarin","orcid":"https://orcid.org/0000-0001-9302-3460"},"institutions":[{"id":"https://openalex.org/I171543936","display_name":"Free University of Bozen-Bolzano","ror":"https://ror.org/012ajp527","country_code":"IT","type":"education","lineage":["https://openalex.org/I171543936"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sofia Casarin","raw_affiliation_strings":["Engineering, Free University of Bozen-Bolzano, Italy"],"raw_orcid":"https://orcid.org/0000-0001-9302-3460","affiliations":[{"raw_affiliation_string":"Engineering, Free University of Bozen-Bolzano, Italy","institution_ids":["https://openalex.org/I171543936"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024044525","display_name":"Cynthia Ifeyinwa Ugwu","orcid":"https://orcid.org/0000-0003-0465-6982"},"institutions":[{"id":"https://openalex.org/I171543936","display_name":"Free University of Bozen-Bolzano","ror":"https://ror.org/012ajp527","country_code":"IT","type":"education","lineage":["https://openalex.org/I171543936"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Cynthia Ugwu","raw_affiliation_strings":["Engineering, Free University of Bozen-Bolzano, Italy"],"raw_orcid":"https://orcid.org/0000-0003-0465-6982","affiliations":[{"raw_affiliation_string":"Engineering, Free University of Bozen-Bolzano, Italy","institution_ids":["https://openalex.org/I171543936"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I171543936"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"111","last_page":"116"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.998199999332428,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980999827384949,"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/mnist-database","display_name":"MNIST database","score":0.8642148971557617},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.768204927444458},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6787829399108887},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6024049520492554},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5885139107704163},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5051231980323792},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.4748839735984802},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4602600038051605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4473862648010254},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.44165608286857605},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.42516207695007324},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08402302861213684}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8642148971557617},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.768204927444458},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6787829399108887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6024049520492554},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5885139107704163},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5051231980323792},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4748839735984802},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4602600038051605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4473862648010254},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.44165608286857605},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.42516207695007324},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08402302861213684},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3653946.3653963","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3653946.3653963","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3653946.3653963","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Machine Vision and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:unibz.it:11327081050001241","is_oa":true,"landing_page_url":"https://bia.unibz.it/esploro/outputs/conferenceProceeding/DDS-NAS-Bench-Towards-predictors-under-Data-Distribution/991006902751601241","pdf_url":null,"source":{"id":"https://openalex.org/S4210197018","display_name":"View","issn_l":"2688-268X","issn":["2688-268X","2688-3988"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Proceedings"}],"best_oa_location":{"id":"doi:10.1145/3653946.3653963","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3653946.3653963","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3653946.3653963","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Machine Vision and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399910982.pdf"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W1985514943","https://openalex.org/W2069870183","https://openalex.org/W2194775991","https://openalex.org/W2964081807","https://openalex.org/W2965658867","https://openalex.org/W3082154327","https://openalex.org/W3107453328","https://openalex.org/W3175547386"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W4388700941"],"abstract_inverted_index":{"Predictor-based":[0],"Neural":[1],"Architecture":[2],"Search":[3],"(NAS)":[4],"proved":[5],"to":[6,19,168],"be":[7,33],"a":[8,42,104,112,119],"fundamental":[9],"topic":[10],"in":[11,159],"the":[12,22,28,61,64,74,80,95,138,140,147],"NAS":[13,98,172],"domain,":[14],"as":[15],"they":[16],"are":[17],"used":[18],"narrow":[20],"down":[21],"number":[23],"of":[24,63,67,71,76,97,122,143],"architectures":[25,123],"for":[26,91,103],"which":[27,48],"true":[29],"validation":[30],"accuracy":[31,52],"must":[32],"computed.":[34],"Prior":[35],"works":[36,58],"on":[37,41],"predictor-based":[38,171],"algorithms":[39],"focus":[40],"single":[43],"proxy":[44],"dataset,":[45],"e.g.":[46],"Cifar-10,":[47,130],"may":[49],"suffer":[50],"from":[51],"decline":[53],"and":[54,78,117,133,146,152,162],"generalization":[55,65],"problems.":[56],"Some":[57],"deal":[59],"with":[60,100],"improvement":[62],"abilities":[66],"predictors,":[68],"however,":[69],"none":[70],"them":[72],"investigate":[73],"possibility":[75],"sharing":[77],"re-using":[79],"predictor":[81],"knowledge":[82],"between":[83,153],"different":[84,127],"datasets.":[85,154],"We":[86,135,155],"impute":[87],"that":[88],"one":[89],"reason":[90],"such":[92],"gap":[93],"is":[94],"absence":[96],"datasets":[99],"distribution":[101],"shifts":[102],"meaningful":[105],"analysis.":[106],"In":[107],"this":[108],"paper,":[109],"we":[110],"propose":[111,163],"new":[113],"search":[114],"space":[115],"definition":[116],"introduce":[118],"dataset":[120],"composed":[121],"trained":[124],"over":[125],"four":[126],"datasets,":[128],"i.e.":[129],"Fashion-MNIST,":[131],"Cifar-100":[132],"Tiny-ImageNET.":[134],"thoroughly":[136],"analyze":[137],"statistics,":[139],"structural":[141],"elements":[142],"successful":[144],"networks,":[145],"ranking":[148],"correlation":[149],"during":[150],"training":[151],"highlight":[156],"clear":[157],"differences":[158],"well-performing":[160],"networks":[161],"an":[164],"early":[165],"stopping":[166],"technique":[167],"seep":[169],"up":[170],"algorithms.":[173]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
