乐胖代购免代理版

{"id":"https://openalex.org/W2024329736","doi":"https://doi.org/10.1109/tpami.2014.2313122","title":"Latent IBP Compound Dirichlet Allocation","display_name":"Latent IBP Compound Dirichlet Allocation","publication_year":2014,"publication_date":"2014-03-29","ids":{"openalex":"https://openalex.org/W2024329736","doi":"https://doi.org/10.1109/tpami.2014.2313122","mag":"2024329736","pmid":"https://pubmed.ncbi.nlm.nih.gov/26353244"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2014.2313122","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://www.gatsby.ucl.ac.uk/~balaji/lida_pami14.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021107714","display_name":"C\u00e9dric Archambeau","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Cedric Archambeau","raw_affiliation_strings":["Amazon (Berlin), Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Amazon (Berlin), Berlin, Germany","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012859570","display_name":"Balaji Lakshminarayanan","orcid":"https://orcid.org/0000-0002-3334-1659"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"funder","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Balaji Lakshminarayanan","raw_affiliation_strings":["[Gatsby Computational Neuroscience Unit, CSML, University College London, London, U.K]"],"affiliations":[{"raw_affiliation_string":"[Gatsby Computational Neuroscience Unit, CSML, University College London, London, U.K]","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090750283","display_name":"Guillaume Bouchard","orcid":"https://orcid.org/0009-0006-5332-0923"},"institutions":[{"id":"https://openalex.org/I33976269","display_name":"Xerox (France)","ror":"https://ror.org/033q0mv79","country_code":"FR","type":"company","lineage":["https://openalex.org/I33976269","https://openalex.org/I4210132870"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Guillaume Bouchard","raw_affiliation_strings":["Xerox Research Centre Europe, Meylan, France#TAB#"],"affiliations":[{"raw_affiliation_string":"Xerox Research Centre Europe, Meylan, France#TAB#","institution_ids":["https://openalex.org/I33976269"]}]}],"institution_assertions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.307,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":29,"citation_normalized_percentile":{"value":0.999969,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":92,"max":93},"biblio":{"volume":"37","issue":"2","first_page":"321","last_page":"333"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9997,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9997,"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/T10594","display_name":"Genetic and phenotypic traits in livestock","score":0.9292,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9221,"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/hierarchical-dirichlet-process","display_name":"Hierarchical Dirichlet process","score":0.8332866},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet Process","score":0.8215941},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.69825935}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.89022726},{"id":"https://openalex.org/C141318989","wikidata":"https://www.wikidata.org/wiki/Q5753066","display_name":"Hierarchical Dirichlet process","level":4,"score":0.8332866},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.8215941},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.69825935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.56639147},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5481848},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5329866},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.5225429},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4122882},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39009798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36766464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27204344},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15169078},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2014.2313122","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.568.3500","pdf_url":"http://www.gatsby.ucl.ac.uk/~balaji/lida_pami14.pdf","source":{"id":"https://openalex.org/S4306400349","display_name":"CiteSeer X (The Pennsylvania State University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I130769515","host_organization_name":"Pennsylvania State University","host_organization_lineage":["https://openalex.org/I130769515"],"host_organization_lineage_names":["Pennsylvania State University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.5927","pdf_url":"http://www0.cs.ucl.ac.uk/staff/c.archambeau/publ/nips_ca11b.pdf","source":{"id":"https://openalex.org/S4306400349","display_name":"CiteSeer X (The Pennsylvania State University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I130769515","host_organization_name":"Pennsylvania State University","host_organization_lineage":["https://openalex.org/I130769515"],"host_organization_lineage_names":["Pennsylvania State University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26353244","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":["National Institutes of Health"],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.568.3500","pdf_url":"http://www.gatsby.ucl.ac.uk/~balaji/lida_pami14.pdf","source":{"id":"https://openalex.org/S4306400349","display_name":"CiteSeer X (The Pennsylvania State University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I130769515","host_organization_name":"Pennsylvania State University","host_organization_lineage":["https://openalex.org/I130769515"],"host_organization_lineage_names":["Pennsylvania State University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.56,"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":44,"referenced_works":["https://openalex.org/W1488394244","https://openalex.org/W1517266559","https://openalex.org/W1547561528","https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1915315806","https://openalex.org/W1934041838","https://openalex.org/W1967687583","https://openalex.org/W1985093013","https://openalex.org/W2001082470","https://openalex.org/W2029443303","https://openalex.org/W2057147815","https://openalex.org/W2069429561","https://openalex.org/W2072169887","https://openalex.org/W2081321853","https://openalex.org/W2087309226","https://openalex.org/W2091797506","https://openalex.org/W2098047130","https://openalex.org/W2104827998","https://openalex.org/W2106480521","https://openalex.org/W2109614047","https://openalex.org/W2113958614","https://openalex.org/W2116350082","https://openalex.org/W2128002512","https://openalex.org/W2128948564","https://openalex.org/W2130843763","https://openalex.org/W2135541598","https://openalex.org/W2151967501","https://openalex.org/W2154099718","https://openalex.org/W2154970197","https://openalex.org/W2158195707","https://openalex.org/W2158266063","https://openalex.org/W2159426623","https://openalex.org/W2160407462","https://openalex.org/W2161050705","https://openalex.org/W2163021329","https://openalex.org/W2167810193","https://openalex.org/W2187741934","https://openalex.org/W2257177755","https://openalex.org/W2334424438","https://openalex.org/W3101200627","https://openalex.org/W4237791300","https://openalex.org/W4292691288","https://openalex.org/W4296300796"],"related_works":["https://openalex.org/W4291700620","https://openalex.org/W2939843948","https://openalex.org/W2914864478","https://openalex.org/W2766840109","https://openalex.org/W2625329765","https://openalex.org/W2352674739","https://openalex.org/W22044811","https://openalex.org/W2097627380","https://openalex.org/W2008338582","https://openalex.org/W1999586157"],"abstract_inverted_index":{"We":[0,45,130],"introduce":[1],"the":[2,29,47,58,70,74,96,101,105,121,142,152,169,180,192],"four-parameter":[3,48],"IBP":[4,85],"compound":[5,86],"Dirichlet":[6,87,148,173],"process":[7,11,124,174],"(ICDP),":[8],"a":[9,117,156],"stochastic":[10],"that":[12,189,200],"generates":[13],"sparse":[14,34,51,118],"non-negative":[15],"vectors":[16],"with":[17,36],"potentially":[18],"an":[19,37,132],"unbounded":[20],"number":[21,39,61,97,106],"of":[22,40,62,73,76,98,107,120,146,159,194],"entries.":[23],"If":[24],"we":[25,31,82],"repeatedly":[26],"sample":[27],"from":[28],"ICDP":[30,49],"can":[32,113],"generate":[33],"matrices":[35],"infinite":[38],"columns":[41],"and":[42,69,103,134,175,199],"power-law":[43,71,92],"characteristics.":[44],"apply":[46],"to":[50,55,127,141,168],"nonparametric":[52,162],"topic":[53,128,164],"modelling":[54],"account":[56],"for":[57,91,191],"very":[59],"large":[60,66],"topics":[63,99],"present":[64],"in":[65,95,104,155],"text":[67],"corpora":[68],"distribution":[72],"vocabulary":[75],"natural":[77],"languages.":[78],"The":[79],"model,":[80],"which":[81],"call":[83],"latent":[84,147],"allocation":[88,149],"(LIDA),":[89],"allows":[90],"distributions,":[93],"both,":[94],"summarising":[100],"documents":[102],"words":[108],"defining":[109],"each":[110],"topic.":[111],"It":[112],"be":[114],"interpreted":[115],"as":[116],"variant":[119],"hierarchical":[122,172,181],"Pitman-Yor":[123,182],"when":[125],"applied":[126],"modelling.":[129],"derive":[131],"efficient":[133],"simple":[135],"collapsed":[136,143],"Gibbs":[137,144],"sampler":[138,145],"closely":[139],"related":[140],"(LDA),":[150],"making":[151],"model":[153,165],"applicable":[154],"wide":[157],"range":[158],"domains.":[160],"Our":[161],"Bayesian":[163],"compares":[166],"favourably":[167],"widely":[170],"used":[171],"its":[176],"heavy":[177],"tailed":[178],"version,":[179],"process,":[183],"on":[184],"benchmark":[185],"corpora.":[186],"Experiments":[187],"demonstrate":[188],"accounting":[190],"power-distribution":[193],"real":[195],"data":[196],"is":[197],"beneficial":[198],"sparsity":[201],"provides":[202],"more":[203],"interpretable":[204],"results.":[205]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2024329736","counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2}],"updated_date":"2025-04-20T13:31:21.507026","created_date":"2016-06-24"}