{"id":"https://openalex.org/W4406462002","doi":"https://doi.org/10.1109/bigdata62323.2024.10825322","title":"LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training","display_name":"LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406462002","doi":"https://doi.org/10.1109/bigdata62323.2024.10825322"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825322","doi":"https://doi.org/10.1109/bigdata62323.2024.10825322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825322","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"IEEE International Conference on Big Data (BigData)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2411.15217","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shreen Gul","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shreen Gul","raw_affiliation_strings":["Missouri University of Science and Technology,Computer Science Department,Rolla,MO,USA,65401"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Computer Science Department,Rolla,MO,USA,65401","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052668861","display_name":"Mohamed Elmahallawy","orcid":"https://orcid.org/0000-0002-5731-9253","observed_orcids":["https://orcid.org/0000-0002-5731-9253"]},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohamed Elmahallawy","raw_affiliation_strings":["Washington State University,School of Engineering & Applied Sciences,Richland,WA,USA,99354"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington State University,School of Engineering & Applied Sciences,Richland,WA,USA,99354","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012569039","display_name":"Sanjay Madria","orcid":"https://orcid.org/0000-0002-2768-3660","observed_orcids":["https://orcid.org/0000-0002-2768-3660"]},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjay Madria","raw_affiliation_strings":["Missouri University of Science and Technology,Computer Science Department,Rolla,MO,USA,65401"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Computer Science Department,Rolla,MO,USA,65401","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056760458","display_name":"Ardhendu Tripathy","orcid":"https://orcid.org/0000-0003-1893-4891","observed_orcids":["https://orcid.org/0000-0003-1893-4891"]},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ardhendu Tripathy","raw_affiliation_strings":["Missouri University of Science and Technology,Computer Science Department,Rolla,MO,USA,65401"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Computer Science Department,Rolla,MO,USA,65401","institution_ids":["https://openalex.org/I20382870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1762,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81645689,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"900","last_page":"909"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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":0.001500000013038516,"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":0.0003000000142492354,"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/active-learning-machine-learning","display_name":"active learning (machine learning)","score":1.0},{"id":"https://openalex.org/keywords/gradient-norm","display_name":"gradient norm","score":0.5860000252723694},{"id":"https://openalex.org/keywords/sample-selection","display_name":"sample selection","score":0.6940000057220459},{"id":"https://openalex.org/keywords/auxiliary-model","display_name":"auxiliary model","score":0.7160000205039978},{"id":"https://openalex.org/keywords/loss-prediction","display_name":"loss prediction","score":0.8489999771118164},{"id":"https://openalex.org/keywords/model-uncertainty","display_name":"model uncertainty","score":0.6859999895095825},{"id":"https://openalex.org/keywords/entropy-information-theory","display_name":"entropy (information theory)","score":0.7509999871253967},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.8629999756813049},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.5820000171661377},{"id":"https://openalex.org/keywords/annotation-cost-reduction","display_name":"annotation cost reduction","score":0.3919999897480011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6477209329605103},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5847978591918945},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5506488680839539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5341687798500061},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5279532670974731},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.526525616645813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.431123822927475},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.41751059889793396},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.07454133033752441},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825322","doi":"https://doi.org/10.1109/bigdata62323.2024.10825322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825322","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"IEEE International Conference on Big Data (BigData)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2411.15217","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2411.15217","pdf_url":"https://arxiv.org/pdf/2411.15217","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2411.15217","doi":"https://doi.org/10.48550/arxiv.2411.15217","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2411.15217","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2411.15217","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2411.15217","pdf_url":"https://arxiv.org/pdf/2411.15217","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406462002.pdf","grobid_xml":"https://content.openalex.org/works/W4406462002.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W2011674654","https://openalex.org/W2119720396","https://openalex.org/W2155904486","https://openalex.org/W2551176409","https://openalex.org/W2594481151","https://openalex.org/W2734358244","https://openalex.org/W2774918944","https://openalex.org/W2798820905","https://openalex.org/W2927805125","https://openalex.org/W2944173897","https://openalex.org/W2990961262","https://openalex.org/W3159399219","https://openalex.org/W3175994565","https://openalex.org/W3180672483","https://openalex.org/W4221138759","https://openalex.org/W4226014440","https://openalex.org/W4283793264","https://openalex.org/W4318824587","https://openalex.org/W4393147441","https://openalex.org/W4393153130","https://openalex.org/W4393159168","https://openalex.org/W4398250170","https://openalex.org/W4406458124","https://openalex.org/W6703116779"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2996078371"],"abstract_inverted_index":{"Machine":[0],"learning":[1,46,155],"models":[2,142],"are":[3,143,222],"increasingly":[4],"being":[5],"utilized":[6],"across":[7],"various":[8],"fields":[9],"and":[10,17,42,78,106,137,175,226,273],"tasks":[11],"due":[12],"to":[13,52,60,101,124,149,170,194,229],"their":[14,22],"outstanding":[15],"performance":[16,236],"strong":[18],"generalization":[19],"capabilities.":[20],"Nonetheless,":[21],"success":[23],"hinges":[24],"on":[25,74,145,243,262],"the":[26,34,63,66,75,108,126,146,151,154,168,180,188,191,203,207,213,218,230,234,248],"availability":[27],"of":[28,31,36,69,110,153,190,206,256,260,266],"large":[29],"volumes":[30],"annotated":[32],"data,":[33],"creation":[35],"which":[37],"is":[38,199,283],"often":[39,58,158],"labor-intensive,":[40],"time-consuming,":[41],"expensive.":[43],"Many":[44],"active":[45],"(AL)":[47],"approaches":[48],"have":[49],"been":[50],"proposed":[51],"address":[53],"these":[54],"challenges,":[55],"but":[56],"they":[57],"fail":[59],"fully":[61],"leverage":[62],"information":[64],"from":[65,179],"core":[67],"phases":[68],"AL,":[70],"such":[71],"as":[72],"training":[73,133,272],"labeled":[76,147,231,267],"set":[77],"querying":[79,274],"new":[80],"unlabeled":[81,214],"samples.":[82],"To":[83],"bridge":[84],"this":[85],"gap,":[86],"we":[87],"propose":[88],"a":[89,134,263],"novel":[90],"AL":[91,162],"approach,":[92],"Loss":[93,95],"Prediction":[94],"with":[96,217,237],"Gradient":[97],"Norm":[98],"(LPLgrad),":[99],"designed":[100],"quantify":[102],"model":[103,136,193],"uncertainty":[104,189],"effectively":[105],"improve":[107],"accuracy":[109,261],"image":[111,278],"classification":[112,279],"tasks.":[113,280],"LPLgrad":[114,249],"operates":[115],"in":[116,160,212,258,276],"two":[117],"distinct":[118],"phases:":[119],"(i)":[120],"Training":[121],"Phase":[122,185],"aims":[123],"predict":[125],"loss":[127],"for":[128,210,224],"input":[129,173],"features":[130,174],"by":[131,201,254],"jointly":[132],"main":[135,192],"an":[138],"auxiliary":[139],"model.":[140],"Both":[141],"trained":[144],"data":[148,181],"maximize":[150],"efficiency":[152],"process\u2014an":[156],"aspect":[157],"overlooked":[159],"previous":[161],"methods.":[163],"This":[164,198],"dual-model":[165],"approach":[166,250],"enhances":[167],"ability":[169],"extract":[171],"complex":[172],"learn":[176],"intrinsic":[177],"patterns":[178],"effectively;":[182],"(ii)":[183],"Querying":[184],"that":[186,247],"quantifies":[187],"guide":[195],"sample":[196],"selection.":[197],"achieved":[200],"calculating":[202],"gradient":[204,220],"norm":[205],"entropy":[208],"values":[209],"samples":[211],"dataset.":[215],"Samples":[216],"highest":[219],"norms":[221],"prioritized":[223],"labeling":[225,239],"subsequently":[227],"added":[228],"set,":[232],"improving":[233],"model\u2019s":[235],"minimal":[238],"effort.":[240],"Extensive":[241],"evaluations":[242],"real-world":[244],"datasets":[245],"demonstrate":[246],"outperforms":[251],"state-of-the-art":[252],"methods":[253],"order":[255],"magnitude":[257],"terms":[259],"small":[264],"number":[265],"images,":[268],"yet":[269],"achieving":[270],"comparable":[271],"times":[275],"multiple":[277],"Our":[281],"code":[282],"available":[284],"at":[285],"Github.":[286]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}