{"id":"https://openalex.org/W4401330410","doi":"https://doi.org/10.1145/3664190.3672515","title":"A Quantum Annealing Instance Selection Approach for Efficient and Effective Transformer Fine-Tuning","display_name":"A Quantum Annealing Instance Selection Approach for Efficient and Effective Transformer Fine-Tuning","publication_year":2024,"publication_date":"2024-08-02","ids":{"openalex":"https://openalex.org/W4401330410","doi":"https://doi.org/10.1145/3664190.3672515"},"language":"en","primary_location":{"id":"doi:10.1145/3664190.3672515","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664190.3672515","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3664190.3672515","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3664190.3672515","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092841298","display_name":"Andrea Pasin","orcid":"https://orcid.org/0009-0007-5193-0741"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Pasin","raw_affiliation_strings":["Universit\u00e0 degli Studi di Padova, Padova, Italy"],"raw_orcid":"https://orcid.org/0009-0007-5193-0741","affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi di Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018690278","display_name":"Washington Cunha","orcid":"https://orcid.org/0000-0002-1988-8412"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Washington Cunha","raw_affiliation_strings":["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-1988-8412","affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046370637","display_name":"Marcos Andr\u00e9 Gon\u00e7alves","orcid":"https://orcid.org/0000-0002-2075-3363"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marcos Andr\u00e9 Gon\u00e7alves","raw_affiliation_strings":["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-2075-3363","affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069843101","display_name":"Nicola Ferro","orcid":"https://orcid.org/0000-0001-9219-6239"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicola Ferro","raw_affiliation_strings":["Universit\u00e0 degli Studi di Padova, Padova, Italy"],"raw_orcid":"https://orcid.org/0000-0001-9219-6239","affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi di Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.673,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.93800096,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"205","last_page":"214"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9991999864578247,"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":0.983299970626831,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9807000160217285,"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":0.8129208087921143},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7485905289649963},{"id":"https://openalex.org/keywords/quantum-annealing","display_name":"Quantum annealing","score":0.7173366546630859},{"id":"https://openalex.org/keywords/quadratic-unconstrained-binary-optimization","display_name":"Quadratic unconstrained binary optimization","score":0.7138513922691345},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7111731767654419},{"id":"https://openalex.org/keywords/quantum-computer","display_name":"Quantum computer","score":0.5286800265312195},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.5140701532363892},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46905186772346497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4668111801147461},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.44235652685165405},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4419592320919037},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37795987725257874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09487703442573547}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8129208087921143},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7485905289649963},{"id":"https://openalex.org/C90408235","wikidata":"https://www.wikidata.org/wiki/Q938141","display_name":"Quantum annealing","level":4,"score":0.7173366546630859},{"id":"https://openalex.org/C177179195","wikidata":"https://www.wikidata.org/wiki/Q7268372","display_name":"Quadratic unconstrained binary optimization","level":4,"score":0.7138513922691345},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7111731767654419},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.5286800265312195},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5140701532363892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46905186772346497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4668111801147461},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.44235652685165405},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4419592320919037},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37795987725257874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09487703442573547},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3664190.3672515","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664190.3672515","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3664190.3672515","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:www.research.unipd.it:11577/3524146","is_oa":true,"landing_page_url":"https://hdl.handle.net/11577/3524146","pdf_url":"https://www.research.unipd.it/bitstream/11577/3524146/2/3664190.3672515.pdf","source":{"id":"https://openalex.org/S4377196283","display_name":"Research Padua  Archive (University of Padua)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"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":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.1145/3664190.3672515","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3664190.3672515","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3664190.3672515","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320322980","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","ror":"https://ror.org/00nc55f03"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401330410.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1965551184","https://openalex.org/W1994410331","https://openalex.org/W2101746535","https://openalex.org/W2115012618","https://openalex.org/W2149308034","https://openalex.org/W2170505850","https://openalex.org/W2750232680","https://openalex.org/W2797779452","https://openalex.org/W2798170028","https://openalex.org/W2908128664","https://openalex.org/W2911319979","https://openalex.org/W2950813464","https://openalex.org/W2970597249","https://openalex.org/W2997591727","https://openalex.org/W3021244424","https://openalex.org/W3034999214","https://openalex.org/W3035183674","https://openalex.org/W3099977377","https://openalex.org/W3105817677","https://openalex.org/W3108273660","https://openalex.org/W3126191299","https://openalex.org/W3184606595","https://openalex.org/W3187038321","https://openalex.org/W4220663753","https://openalex.org/W4226006885","https://openalex.org/W4284699573","https://openalex.org/W4285729218","https://openalex.org/W4288072953","https://openalex.org/W4293103067","https://openalex.org/W4365459368","https://openalex.org/W4384639909","https://openalex.org/W4386579179","https://openalex.org/W4386585738","https://openalex.org/W4389519914"],"related_works":["https://openalex.org/W4380986281","https://openalex.org/W4316012214","https://openalex.org/W2917807404","https://openalex.org/W2893961859","https://openalex.org/W4388555437","https://openalex.org/W2006098914","https://openalex.org/W4401855577","https://openalex.org/W4287027624","https://openalex.org/W4389168364","https://openalex.org/W4385585333"],"abstract_inverted_index":{"Deep":[0],"Learning":[1],"approaches":[2],"have":[3,125],"become":[4],"pervasive":[5],"in":[6,90,156],"recent":[7],"years":[8],"due":[9],"to":[10,13,62,73,114,130],"their":[11],"ability":[12],"solve":[14],"complex":[15,43],"tasks.":[16],"However,":[17],"these":[18],"models":[19,44],"need":[20],"huge":[21],"datasets":[22,70],"for":[23,40,148],"proper":[24],"training":[25,33,69],"and":[26,34,45,84,176],"good":[27],"generalization.":[28],"This":[29],"translates":[30],"into":[31],"high":[32],"fine-tuning":[35],"time,":[36],"even":[37],"several":[38,165],"days":[39],"the":[41,65,68,77,91,119,132,149,179],"most":[42],"large":[46],"datasets.":[47],"In":[48],"this":[49],"work,":[50],"we":[51,138,169],"present":[52],"a":[53,96,105,140,154],"novel":[54],"quantum":[55,173],"Instance":[56],"Selection":[57],"(IS)":[58],"approach":[59],"that":[60,93,110],"allows":[61],"significantly":[63],"reduce":[64],"size":[66],"of":[67,121,162],"(by":[71],"up":[72],"28%)":[74],"while":[75],"maintaining":[76],"model's":[78],"effectiveness,":[79],"thus":[80],"promoting":[81],"(training)":[82],"speedups":[83],"scalability.":[85],"Our":[86],"solution":[87],"is":[88,153],"innovative":[89],"sense":[92],"it":[94],"exploits":[95],"different":[97],"computing":[98],"paradigm":[99,109],"-":[100,104],"Quantum":[101,107],"Annealing":[102],"(QA)":[103],"specific":[106,147],"Computing":[108],"can":[111],"be":[112],"used":[113],"tackle":[115,131],"optimization":[116],"problems.":[117],"To":[118],"best":[120],"our":[122,172],"knowledge,":[123],"there":[124],"been":[126],"no":[127],"prior":[128],"attempts":[129],"IS":[133,150,182],"problem":[134],"using":[135],"QA.":[136],"Furthermore,":[137],"propose":[139],"new":[141],"Quadratic":[142],"Unconstrained":[143],"Binary":[144],"Optimization":[145],"formulation":[146],"problem,":[151],"which":[152],"contribution":[155],"itself.":[157],"Through":[158],"an":[159],"extensive":[160],"set":[161],"experiments":[163],"with":[164,178],"Text":[166],"Classification":[167],"benchmarks,":[168],"empirically":[170],"demonstrate":[171],"solution's":[174],"feasibility":[175],"competitiveness":[177],"current":[180],"state-of-the-art":[181],"solutions.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2024-08-06T00:00:00"}
