{"id":"https://openalex.org/W2995349754","doi":"https://doi.org/10.48550/arxiv.2004.12289","title":"Deep k-NN for Noisy Labels","display_name":"Deep k-NN for Noisy Labels","publication_year":2020,"publication_date":"2020-04-26","ids":{"openalex":"https://openalex.org/W2995349754","doi":"https://doi.org/10.48550/arxiv.2004.12289","mag":"2995349754"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2004.12289","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.12289","pdf_url":"https://arxiv.org/pdf/2004.12289","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"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.12289","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036477705","display_name":"Dara Bahri","orcid":"https://orcid.org/0000-0003-0144-2911","observed_orcids":["https://orcid.org/0000-0003-0144-2911"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bahri, Dara","raw_affiliation_strings":["Google,,,,,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google,,,,,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067496870","display_name":"Heinrich Jiang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiang, Heinrich","raw_affiliation_strings":["Google Research,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111921762","display_name":"Maya R. Gupta","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gupta, Maya","raw_affiliation_strings":["Google,,,,,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google,,,,,","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"540","last_page":"550"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9994999766349792,"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":0.9994999766349792,"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/T11689","display_name":"Adversarial Robustness in Machine 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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noisy-labels","display_name":"label noise","score":0.9860000014305115},{"id":"https://openalex.org/keywords/mislabeled-training-data","display_name":"mislabeled training data","score":0.7310000061988831},{"id":"https://openalex.org/keywords/statistical-guarantees","display_name":"statistical guarantees","score":0.8799999952316284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7773876786231995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.690863847732544},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.6492956876754761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6040212512016296},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5024864673614502},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.4987175464630127},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.46039456129074097},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.4172775149345398},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3473593592643738},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"pmh:oai:arXiv.org:2004.12289","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.12289","pdf_url":"https://arxiv.org/pdf/2004.12289","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"},{"id":"mag:2995349754","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2004.12289.pdf","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2004.12289","doi":"https://doi.org/10.48550/arxiv.2004.12289","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2004.12289","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.57702/s6if20k1","doi":"https://doi.org/10.57702/s6if20k1","is_oa":true,"landing_page_url":"https://doi.org/10.57702/s6if20k1","pdf_url":null,"source":{"id":"https://openalex.org/S7407053387","display_name":"TIB Data Manager","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2802635041","host_organization_name":"Technische Informationsbibliothek (TIB)","host_organization_lineage":["https://openalex.org/I2802635041"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"},{"id":"mag:3035611495","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v119/bahri20a/bahri20a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"International Conference on Machine Learning","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.12289","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.12289","pdf_url":"https://arxiv.org/pdf/2004.12289","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":43,"referenced_works":["https://openalex.org/W369786348","https://openalex.org/W375096984","https://openalex.org/W1514928307","https://openalex.org/W1591052160","https://openalex.org/W1866072925","https://openalex.org/W1921293667","https://openalex.org/W1996437515","https://openalex.org/W2032708784","https://openalex.org/W2043919728","https://openalex.org/W2049393399","https://openalex.org/W2059507684","https://openalex.org/W2092260586","https://openalex.org/W2107686700","https://openalex.org/W2108882154","https://openalex.org/W2113290770","https://openalex.org/W2136688338","https://openalex.org/W2146396044","https://openalex.org/W2437617937","https://openalex.org/W2493144170","https://openalex.org/W2592335154","https://openalex.org/W2618574054","https://openalex.org/W2626166918","https://openalex.org/W2772357980","https://openalex.org/W2775306753","https://openalex.org/W2793165286","https://openalex.org/W2795282075","https://openalex.org/W2804378530","https://openalex.org/W2806075129","https://openalex.org/W2885593519","https://openalex.org/W2891461240","https://openalex.org/W2899039999","https://openalex.org/W2914448714","https://openalex.org/W2945049737","https://openalex.org/W2946376679","https://openalex.org/W2952927437","https://openalex.org/W2963697299","https://openalex.org/W2963759070","https://openalex.org/W2964096266","https://openalex.org/W2964273174","https://openalex.org/W2964292098","https://openalex.org/W2964309657","https://openalex.org/W2970308742","https://openalex.org/W3100570787"],"related_works":["https://openalex.org/W3035611495","https://openalex.org/W3127003832","https://openalex.org/W2964155802","https://openalex.org/W3105982656","https://openalex.org/W3104126699","https://openalex.org/W3108420046","https://openalex.org/W2592335154","https://openalex.org/W2795241978","https://openalex.org/W2971450851","https://openalex.org/W3186719914","https://openalex.org/W3195726023","https://openalex.org/W3136766697","https://openalex.org/W3173540179","https://openalex.org/W3205333200","https://openalex.org/W3179042185","https://openalex.org/W3090046825","https://openalex.org/W3139144418","https://openalex.org/W3089931876","https://openalex.org/W2583188830","https://openalex.org/W3013573099"],"abstract_inverted_index":{"Modern":[0],"machine":[1],"learning":[2],"models":[3,53],"are":[4,16],"often":[5],"trained":[6],"on":[7,36],"examples":[8],"with":[9],"noisy":[10],"labels":[11],"that":[12,29],"hurt":[13],"performance":[14],"and":[15,49],"hard":[17],"to":[18],"identify.":[19],"In":[20],"this":[21],"paper,":[22],"we":[23],"provide":[24,61],"an":[25],"empirical":[26],"study":[27],"showing":[28],"a":[30,41],"simple":[31],"$k$-nearest":[32],"neighbor-based":[33],"filtering":[34],"approach":[35],"the":[37],"logit":[38],"layer":[39],"of":[40],"preliminary":[42],"model":[43],"can":[44],"remove":[45],"mislabeled":[46],"training":[47],"data":[48],"produce":[50],"more":[51],"accurate":[52],"than":[54],"many":[55],"recently":[56],"proposed":[57],"methods.":[58],"We":[59],"also":[60],"new":[62],"statistical":[63],"guarantees":[64],"into":[65],"its":[66],"efficacy.":[67]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-09-29T14:09:16.559479","created_date":"2019-12-26T00:00:00"}