{"id":"https://openalex.org/W2788739741","doi":"https://doi.org/10.1145/3178876.3186033","title":"Leveraging Crowdsourcing Data for Deep Active Learning An Application","display_name":"Leveraging Crowdsourcing Data for Deep Active Learning An Application","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2788739741","doi":"https://doi.org/10.1145/3178876.3186033","mag":"2788739741"},"language":"en","primary_location":{"id":"doi:10.1145/3178876.3186033","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186033","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186033&type=pdf","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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3186033&type=pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jie Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["Delft University of Technology, Delft, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Thomas Drake","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Drake","raw_affiliation_strings":["Amazon Research, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Research, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Andreas Damianou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123934","display_name":"Amazon (United Kingdom)","ror":"https://ror.org/02xey9634","country_code":"GB","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210123934"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Andreas Damianou","raw_affiliation_strings":["Amazon Research, Cambridge, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Research, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I4210123934"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yoelle Maarek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoelle Maarek","raw_affiliation_strings":["Amazon Research, Haifa, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Research, Haifa, Israel","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.5033,"has_fulltext":true,"cited_by_count":49,"citation_normalized_percentile":{"value":0.97375313,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"32"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.9968000054359436,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9959999918937683,"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/crowdsourcing","display_name":"Crowdsourcing","score":0.886900007724762},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7142999768257141},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5726000070571899},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.5440999865531921},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5040000081062317},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4830000102519989},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4677000045776367},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42809998989105225}],"concepts":[{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.886900007724762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8343999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7767999768257141},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7142999768257141},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6872000098228455},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5726000070571899},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3926999866962433},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2784000039100647},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3178876.3186033","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186033","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186033&type=pdf","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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1803.04223","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1803.04223","pdf_url":"https://arxiv.org/pdf/1803.04223","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"doi:10.1145/3178876.3186033","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3178876.3186033","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3186033&type=pdf","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 2018 World Wide Web Conference on World Wide Web - WWW '18","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2788739741.pdf","grobid_xml":"https://content.openalex.org/works/W2788739741.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W9014458","https://openalex.org/W49514851","https://openalex.org/W831822886","https://openalex.org/W1989747167","https://openalex.org/W1995470018","https://openalex.org/W2022710553","https://openalex.org/W2035683813","https://openalex.org/W2064677871","https://openalex.org/W2096519149","https://openalex.org/W2113878109","https://openalex.org/W2726137762","https://openalex.org/W2949071206","https://openalex.org/W4211096099"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,81],"generic":[4],"Bayesian":[5,26,83],"framework":[6,20,54,107,147,166,198],"that":[7,164],"enables":[8],"any":[9],"deep":[10,27,94,113,138],"learning":[11,95,103,114,139,202],"model":[12,84,96,115,183],"to":[13,61,69,85,99,125,187],"actively":[14],"learn":[15,62,169],"from":[16,22,74],"targeted":[17,36],"crowds.":[18],"Our":[19,53],"inherits":[21],"recent":[23],"advances":[24],"in":[25,59,97,151,182,199],"learning,":[28],"and":[29,76,91,132,159,175,203],"extends":[30],"existing":[31],"work":[32],"by":[33],"considering":[34],"the":[35,56,71,88,93,109,112,121,127,137,143,178,193],"crowdsourcing":[37,204],"approach,":[38],"where":[39],"multiple":[40],"annotators":[41,133],"with":[42],"unknown":[43],"expertise":[44,124],"contribute":[45],"an":[46,101],"uncontrolled":[47],"amount":[48,179],"(often":[49],"limited)":[50],"of":[51,111,129,145,180,195],"annotations.":[52,78],"leverages":[55],"low-rank":[57],"structure":[58],"annotations":[60,131,181],"individual":[63],"annotator":[64,170],"expertise,":[65,171],"which":[66],"then":[67],"helps":[68],"infer":[70,87,172],"true":[72,89,173],"labels":[73,90],"noisy":[75],"sparse":[77],"It":[79],"provides":[80],"unified":[82],"simultaneously":[86],"train":[92],"order":[98],"reach":[100],"optimal":[102],"efficacy.":[104],"Finally,":[105],"our":[106,146,165,196],"exploits":[108],"uncertainty":[110],"during":[116],"prediction":[117],"as":[118,120,185],"well":[119],"annotators\u00bb":[122],"estimated":[123],"minimize":[126],"number":[128],"required":[130],"for":[134,148],"optimally":[135],"training":[136,184],"model.":[140],"We":[141,190],"evaluate":[142],"effectiveness":[144],"intent":[149],"classification":[150],"Alexa":[152],"(Amazon\u00bbs":[153],"personal":[154],"assistant),":[155],"using":[156],"both":[157],"synthetic":[158],"real-world":[160],"datasets.":[161],"Experiments":[162],"show":[163],"can":[167],"accurately":[168],"labels,":[174],"effectively":[176],"reduce":[177],"compared":[186],"state-of-the-art":[188],"approaches.":[189],"further":[191],"discuss":[192],"potential":[194],"proposed":[197],"bridging":[200],"machine":[201],"towards":[205],"improved":[206],"human-in-the-loop":[207],"systems.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2018-03-06T00:00:00"}
