{"id":"https://openalex.org/W3118094271","doi":"https://doi.org/10.1109/csci51800.2020.00084","title":"Identifying the Training Stop Point with Noisy Labeled Data","display_name":"Identifying the Training Stop Point with Noisy Labeled Data","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3118094271","doi":"https://doi.org/10.1109/csci51800.2020.00084","mag":"3118094271"},"language":"en","primary_location":{"id":"doi:10.1109/csci51800.2020.00084","doi":"https://doi.org/10.1109/csci51800.2020.00084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/csci51800.2020.00084","pdf_url":null,"source":{"id":"https://openalex.org/S4363607974","display_name":"International Conference on Computational Science and Computational Intelligence (CSCI)","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":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.13435","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093948700","display_name":"Sreeram Kamabattula","orcid":"https://orcid.org/0000-0001-9153-9156","observed_orcids":["https://orcid.org/0000-0001-9153-9156"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sree Ram Kamabattula","raw_affiliation_strings":["The Univeristy of Texas at Arlington,Department of Electrical Engineering,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univeristy of Texas at Arlington,Department of Electrical Engineering,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Venkat Devarajan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Venkat Devarajan","raw_affiliation_strings":["The Univeristy of Texas at Arlington,Department of Electrical Engineering,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univeristy of Texas at Arlington,Department of Electrical Engineering,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Babak Namazi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210160418","display_name":"Baylor Scott & White Health","ror":"https://ror.org/05wevan27","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210160418"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Babak Namazi","raw_affiliation_strings":["Baylor Scott and White Health,Dallas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baylor Scott and White Health,Dallas,USA","institution_ids":["https://openalex.org/I4210160418"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079931660","display_name":"Ganesh Sankaranarayanan","orcid":"https://orcid.org/0000-0003-1556-2797","observed_orcids":["https://orcid.org/0000-0003-1556-2797"]},"institutions":[{"id":"https://openalex.org/I4210160418","display_name":"Baylor Scott & White Health","ror":"https://ror.org/05wevan27","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210160418"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ganesh Sankaranarayanan","raw_affiliation_strings":["Baylor Scott and White Health,Dallas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baylor Scott and White Health,Dallas,USA","institution_ids":["https://openalex.org/I4210160418"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1908,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48468755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":"8","issue":null,"first_page":"457","last_page":"463"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9984999895095825,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9984999895095825,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.00039999998989515007,"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":0.00019999999494757503,"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/early-stopping","display_name":"early stopping","score":0.7599999904632568},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":0.6290000081062317},{"id":"https://openalex.org/keywords/noisy-labels","display_name":"label noise","score":0.4860000014305115},{"id":"https://openalex.org/keywords/noise-estimation","display_name":"noise estimation","score":0.4569999873638153}],"concepts":[{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.692300021648407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6456999778747559},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6449000239372253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.63919997215271},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5681999921798706},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5087000131607056},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46939998865127563},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4569999873638153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.42969998717308044},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4189999997615814},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.37929999828338623},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.37070000171661377},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3212999999523163},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31929999589920044},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C2781415353","wikidata":"https://www.wikidata.org/wiki/Q5196602","display_name":"Cut-point","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2984485829","wikidata":"https://www.wikidata.org/wiki/Q100766421","display_name":"Noise level","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/csci51800.2020.00084","doi":"https://doi.org/10.1109/csci51800.2020.00084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/csci51800.2020.00084","pdf_url":null,"source":{"id":"https://openalex.org/S4363607974","display_name":"International Conference on Computational Science and Computational Intelligence (CSCI)","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":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2012.13435","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.13435","pdf_url":"https://arxiv.org/pdf/2012.13435","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2012.13435","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.13435","pdf_url":"https://arxiv.org/pdf/2012.13435","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":null,"license_id":null,"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":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1921293667","https://openalex.org/W1972675781","https://openalex.org/W2065151783","https://openalex.org/W2121056381","https://openalex.org/W2194775991","https://openalex.org/W2609701267","https://openalex.org/W2618574054","https://openalex.org/W2682189153","https://openalex.org/W2752971446","https://openalex.org/W2795282075","https://openalex.org/W2803187616","https://openalex.org/W2804077623","https://openalex.org/W2885593519","https://openalex.org/W2909793444","https://openalex.org/W2925181975","https://openalex.org/W2943997064","https://openalex.org/W2945007112","https://openalex.org/W2950220847","https://openalex.org/W2962762068","https://openalex.org/W2963735582","https://openalex.org/W2964234160","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2990476713","https://openalex.org/W2991599149","https://openalex.org/W2996060033","https://openalex.org/W2996108195"],"related_works":[],"abstract_inverted_index":{"Finding":[0],"an":[1],"early":[2],"stopping":[3],"point":[4,106],"at":[5,108],"maximum":[6],"obtainable":[7],"test":[8],"accuracy":[9,84],"(MOTA)":[10],"is":[11,35,42,52],"a":[12,31,48,67,90,97,103],"challenging":[13],"problem":[14],"when":[15],"training":[16,69,83,91,104],"deep":[17],"neural":[18],"networks":[19],"(DNNs)":[20],"with":[21],"noisy":[22],"labeled":[23],"data.":[24],"Recent":[25],"studies":[26],"assume":[27],"either":[28],"that":[29],"i)":[30],"clean":[32,49],"validation":[33,50],"set":[34,51],"available":[36],"or":[37,109],"ii)":[38],"the":[39,55,77,82,115],"noise":[40,56,125],"ratio":[41],"known,":[43],"or,":[44],"both.":[45],"However,":[46],"often":[47],"unavailable,":[53],"and":[54,127],"estimation":[57],"can":[58],"be":[59],"inaccurate.":[60],"To":[61],"overcome":[62],"these":[63,73],"issues,":[64],"we":[65],"provide":[66],"novel":[68],"solution,":[70],"free":[71],"of":[72,79,81,117],"conditions.":[74],"We":[75,94,113],"analyze":[76],"rate":[78],"change":[80],"under":[85],"different":[86],"conditions":[87],"to":[88,101,111],"identify":[89],"stop":[92,105],"region.":[93],"further":[95],"develop":[96],"heuristic":[98],"algorithm":[99],"(AutoTSP)":[100],"find":[102],"(TSP)":[107],"close":[110],"MOTA.":[112],"validate":[114],"robustness":[116],"AutoTSP":[118],"through":[119],"several":[120],"experiments":[121],"on":[122],"various":[123],"datasets,":[124],"ratios":[126],"architectures.":[128]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-01-05T00:00:00"}
