{"id":"https://openalex.org/W4401881371","doi":"https://doi.org/10.1109/compsac61105.2024.00278","title":"Relative Performance Prediction Using Few-Shot Learning","display_name":"Relative Performance Prediction Using Few-Shot Learning","publication_year":2024,"publication_date":"2024-07-02","ids":{"openalex":"https://openalex.org/W4401881371","doi":"https://doi.org/10.1109/compsac61105.2024.00278"},"language":"en","primary_location":{"id":"doi:10.1109/compsac61105.2024.00278","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/compsac61105.2024.00278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)","raw_type":"proceedings-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/A5077393435","display_name":"Arunavo Dey","orcid":"https://orcid.org/0000-0003-2319-586X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Arunavo Dey","raw_affiliation_strings":["Texas State University"],"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092316758","display_name":"Aakash Raj Dhakal","orcid":"https://orcid.org/0009-0006-2997-8434"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aakash Dhakal","raw_affiliation_strings":["Texas State University"],"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002465410","display_name":"Tanzima Islam","orcid":"https://orcid.org/0000-0003-2877-5871"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tanzima Z. Islam","raw_affiliation_strings":["Texas State University"],"affiliations":[{"raw_affiliation_string":"Texas State University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108398863","display_name":"Jae-Seung Yeom","orcid":null},"institutions":[{"id":"https://openalex.org/I1282311441","display_name":"Lawrence Livermore National Laboratory","ror":"https://ror.org/041nk4h53","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282311441","https://openalex.org/I1330989302","https://openalex.org/I198811213","https://openalex.org/I4210138311"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jae-Seung Yeom","raw_affiliation_strings":["Lawrence Livermore National Laboratory"],"affiliations":[{"raw_affiliation_string":"Lawrence Livermore National Laboratory","institution_ids":["https://openalex.org/I1282311441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082396646","display_name":"Tapasya Patki","orcid":"https://orcid.org/0000-0003-2543-9688"},"institutions":[{"id":"https://openalex.org/I1282311441","display_name":"Lawrence Livermore National Laboratory","ror":"https://ror.org/041nk4h53","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282311441","https://openalex.org/I1330989302","https://openalex.org/I198811213","https://openalex.org/I4210138311"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tapasya Patki","raw_affiliation_strings":["Lawrence Livermore National Laboratory"],"affiliations":[{"raw_affiliation_string":"Lawrence Livermore National Laboratory","institution_ids":["https://openalex.org/I1282311441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068348488","display_name":"Daniel Nichols","orcid":"https://orcid.org/0000-0002-3538-6164"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Nichols","raw_affiliation_strings":["University of Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092230844","display_name":"Alexander Movsesyan","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Movsesyan","raw_affiliation_strings":["University of Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081506338","display_name":"Abhinav Bhatel\u00e9","orcid":"https://orcid.org/0000-0003-3069-3701"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Bhatele","raw_affiliation_strings":["University of Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5077393435"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7274,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75664711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1764","last_page":"1769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9945999979972839,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9945999979972839,"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/T12676","display_name":"Machine Learning and ELM","score":0.9384999871253967,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9120000004768372,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.727595865726471},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6530650854110718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239399075508118},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38631004095077515},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.049748361110687256}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.727595865726471},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6530650854110718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239399075508118},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38631004095077515},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.049748361110687256},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/compsac61105.2024.00278","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/compsac61105.2024.00278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)","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":15,"referenced_works":["https://openalex.org/W1966128391","https://openalex.org/W2017733144","https://openalex.org/W2040356714","https://openalex.org/W2107158148","https://openalex.org/W2163345210","https://openalex.org/W2239144794","https://openalex.org/W2605705966","https://openalex.org/W2767224631","https://openalex.org/W2966274836","https://openalex.org/W3011096293","https://openalex.org/W3028644320","https://openalex.org/W3034942609","https://openalex.org/W3038118166","https://openalex.org/W4306178203","https://openalex.org/W4399455343"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"High-performance":[0],"computing":[1],"system":[2],"architectures":[3,58],"are":[4],"evolving":[5],"rapidly,":[6],"making":[7,41],"exhaustive":[8],"data":[9,34,61],"collection":[10,35],"for":[11,45,85],"each":[12],"architecture":[13],"to":[14,47,51,65,98],"build":[15],"predictive":[16],"performance":[17,30,55,102],"models":[18,50,97,128],"increasingly":[19],"impractical.":[20],"Concurrently,":[21],"the":[22,69,81,100],"arrival":[23],"of":[24,103],"new":[25,57,104,107],"applications":[26,76,105],"daily":[27],"necessitates":[28],"efficient":[29,44],"prediction":[31],"methods.":[32],"Traditional":[33],"can":[36,138],"take":[37],"days":[38],"or":[39,59],"weeks,":[40],"it":[42],"more":[43],"scientists":[46],"leverage":[48],"existing":[49,110],"predict":[52,66,99],"an":[53],"application's":[54],"on":[56,68,106],"use":[60],"from":[62],"one":[63],"application":[64],"another":[67],"same":[70],"architecture.":[71],"The":[72],"growing":[73],"heterogeneity":[74],"in":[75],"and":[77,132],"resources":[78],"further":[79],"complicates":[80],"exact":[82],"matches":[83],"needed":[84],"effective":[86],"knowledge":[87,125,141],"transfer.":[88],"This":[89],"work":[90],"systematically":[91],"studies":[92],"various":[93],"Machine":[94],"Learning":[95],"(ML)":[96],"relative":[101],"platforms":[108],"using":[109,118],"data.":[111],"Our":[112],"findings":[113],"demonstrate":[114],"that":[115],"few-shot":[116],"learning":[117],"a":[119],"few":[120],"samples":[121,137],"significantly":[122],"enhances":[123],"cross-platform":[124],"transfer,":[126],"multi-source":[127],"outperform":[129],"single-source":[130],"models,":[131],"Large":[133],"Language":[134],"Models":[135],"(LLMs)-generated":[136],"effectively":[139],"improve":[140],"transfer":[142],"efficacy.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}