{"id":"https://openalex.org/W4401863624","doi":"https://doi.org/10.1145/3637528.3671876","title":"Top-Down Bayesian Posterior Sampling for Sum-Product Networks","display_name":"Top-Down Bayesian Posterior Sampling for Sum-Product Networks","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863624","doi":"https://doi.org/10.1145/3637528.3671876"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671876","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051375854","display_name":"Soma Yokoi","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Soma Yokoi","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060421432","display_name":"Issei Sato","orcid":"https://orcid.org/0000-0002-5066-1435"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Issei Sato","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051375854"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11214485,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3977","last_page":"3988"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9951000213623047,"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":0.9951000213623047,"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":0.991599977016449,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.986299991607666,"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/computer-science","display_name":"Computer science","score":0.6551628112792969},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6249348521232605},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5309616327285767},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4940793812274933},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4736301302909851},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.46907514333724976},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3313622772693634},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3247542381286621},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.320232093334198},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22162070870399475},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10122328996658325}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6551628112792969},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6249348521232605},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5309616327285767},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4940793812274933},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4736301302909851},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.46907514333724976},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3313622772693634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3247542381286621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.320232093334198},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22162070870399475},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10122328996658325},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671876","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2010438070","https://openalex.org/W2040370888","https://openalex.org/W2070450685","https://openalex.org/W2132862423","https://openalex.org/W2196348858","https://openalex.org/W2290998037","https://openalex.org/W2403625461","https://openalex.org/W2597851033","https://openalex.org/W2606098075","https://openalex.org/W2608702473","https://openalex.org/W2908860143","https://openalex.org/W2963276753","https://openalex.org/W2963368172","https://openalex.org/W2963375846","https://openalex.org/W2968371142","https://openalex.org/W2978329087","https://openalex.org/W3104062571","https://openalex.org/W4247950230","https://openalex.org/W4299828299","https://openalex.org/W4310895557"],"related_works":["https://openalex.org/W2972991241","https://openalex.org/W2381390841","https://openalex.org/W2354548288","https://openalex.org/W4367181073","https://openalex.org/W2032094637","https://openalex.org/W2040227828","https://openalex.org/W2060045818","https://openalex.org/W2131935101","https://openalex.org/W856257623","https://openalex.org/W2892315154"],"abstract_inverted_index":{"Sum-product":[0],"networks":[1],"(SPNs)":[2],"are":[3],"probabilistic":[4,14],"models":[5],"characterized":[6],"by":[7,91],"exact":[8],"and":[9,37,54,133,145,157],"fast":[10,46],"evaluation":[11],"of":[12,43,88,129],"fundamental":[13],"operations.":[15],"Its":[16],"superior":[17,158],"computational":[18,147],"tractability":[19],"has":[20,141],"led":[21],"to":[22,50,59,67,96,150],"applications":[23],"in":[24,136,161],"many":[25],"fields,":[26],"such":[27],"as":[28],"machine":[29],"learning":[30,71],"with":[31],"time":[32],"constraints":[33,42],"or":[34],"accuracy":[35],"requirements":[36],"real-time":[38],"systems.":[39],"The":[40,102],"structural":[41],"SPNs":[44],"supporting":[45],"inference,":[47],"however,":[48],"lead":[49],"increased":[51],"learning-time":[52,143],"complexity":[53,103,144],"can":[55,74],"be":[56,75],"an":[57],"obstacle":[58],"building":[60],"highly":[61],"expressive":[62],"SPNs.":[63,80,138],"This":[64],"study":[65],"aimed":[66],"develop":[68],"a":[69,83,121],"Bayesian":[70],"approach":[72],"that":[73,106,125],"efficiently":[76,111],"implemented":[77],"on":[78,164],"large-scale":[79,137],"We":[81],"derived":[82],"new":[84],"full":[85],"conditional":[86],"probability":[87],"Gibbs":[89],"sampling":[90,108],"marginalizing":[92],"multiple":[93],"random":[94],"variables":[95],"expeditiously":[97],"obtain":[98],"the":[99,114,127,130],"posterior":[100],"distribution.":[101],"analysis":[104],"revealed":[105],"our":[107],"algorithm":[109],"works":[110],"even":[112],"for":[113],"largest":[115],"possible":[116],"SPN.":[117],"Furthermore,":[118],"we":[119],"proposed":[120],"hyperparameter":[122],"tuning":[123],"method":[124,140],"balances":[126],"diversity":[128],"prior":[131],"distribution":[132],"optimization":[134],"efficiency":[135],"Our":[139],"improved":[142],"demonstrated":[146],"speed":[148],"tens":[149],"more":[151,165],"than":[152,166],"one":[153],"hundred":[154],"times":[155],"faster":[156],"predictive":[159],"performance":[160],"numerical":[162],"experiments":[163],"20":[167],"datasets.":[168]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}