{"id":"https://openalex.org/W3035215391","doi":"https://doi.org/10.1109/tkde.2020.3000559","title":"Deterministic Inference of Topic Models via Maximal Latent State Replication","display_name":"Deterministic Inference of Topic Models via Maximal Latent State Replication","publication_year":2020,"publication_date":"2020-06-08","ids":{"openalex":"https://openalex.org/W3035215391","doi":"https://doi.org/10.1109/tkde.2020.3000559","mag":"3035215391"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2020.3000559","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3000559","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-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/A5081503859","display_name":"Daniel Rugeles","orcid":"https://orcid.org/0000-0001-8085-3123"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Daniel Rugeles","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University, Singapore","[School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore Singapore (e-mail: danrugeles@gmail.com)]"],"raw_orcid":"https://orcid.org/0000-0001-8085-3123","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"[School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore Singapore (e-mail: danrugeles@gmail.com)]","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025881601","display_name":"Zhen Hai","orcid":"https://orcid.org/0000-0003-3582-4563"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhen Hai","raw_affiliation_strings":["Institute of Infocomm Research, A*Star, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-3582-4563","affiliations":[{"raw_affiliation_string":"Institute of Infocomm Research, A*Star, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043358521","display_name":"Manoranjan Dash","orcid":"https://orcid.org/0000-0003-1216-8700"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Manoranjan Dash","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045198704","display_name":"Gao Cong","orcid":"https://orcid.org/0000-0002-4430-6373"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Gao Cong","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-4430-6373","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":0.2844,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67467629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"34","issue":"4","first_page":"1684","last_page":"1695"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.996399998664856,"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/T11550","display_name":"Text and Document Classification Technologies","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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8304076790809631},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.8024518489837646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7930960655212402},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7898727655410767},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.6418317556381226},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.634763777256012},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5978724956512451},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5610637664794922},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.495098739862442},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.470975399017334},{"id":"https://openalex.org/keywords/predictive-inference","display_name":"Predictive inference","score":0.41516003012657166},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4079012870788574},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.3622260093688965},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.299416184425354},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.13494369387626648},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.12335655093193054}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8304076790809631},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8024518489837646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7930960655212402},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7898727655410767},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.6418317556381226},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.634763777256012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5978724956512451},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5610637664794922},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.495098739862442},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.470975399017334},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.41516003012657166},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4079012870788574},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.3622260093688965},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.299416184425354},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.13494369387626648},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.12335655093193054}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2020.3000559","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3000559","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4256565688","display_name":null,"funder_award_id":"20132014S10797","funder_id":"https://openalex.org/F4320324110","funder_display_name":"Singapore University of Technology and Design"}],"funders":[{"id":"https://openalex.org/F4320324110","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1511187233","https://openalex.org/W1609010894","https://openalex.org/W1650067024","https://openalex.org/W2001082470","https://openalex.org/W2007321142","https://openalex.org/W2021197309","https://openalex.org/W2041517243","https://openalex.org/W2052261215","https://openalex.org/W2059638643","https://openalex.org/W2065221212","https://openalex.org/W2070603970","https://openalex.org/W2129004009","https://openalex.org/W2130428211","https://openalex.org/W2146341620","https://openalex.org/W2150731624","https://openalex.org/W2158919786","https://openalex.org/W2250533720","https://openalex.org/W2259123132","https://openalex.org/W2588293823","https://openalex.org/W2605000734","https://openalex.org/W2951681883","https://openalex.org/W2963558938","https://openalex.org/W4231517135","https://openalex.org/W4231989864","https://openalex.org/W4237791300","https://openalex.org/W4289437843","https://openalex.org/W6600144256","https://openalex.org/W6631644986","https://openalex.org/W6675861922","https://openalex.org/W6678337573","https://openalex.org/W6679226638","https://openalex.org/W6681349557","https://openalex.org/W6682044806","https://openalex.org/W6683847445","https://openalex.org/W6684489972","https://openalex.org/W6684809622","https://openalex.org/W6715131761","https://openalex.org/W6736124316","https://openalex.org/W6754320333"],"related_works":["https://openalex.org/W1551384396","https://openalex.org/W4206967254","https://openalex.org/W4293734197","https://openalex.org/W2096865229","https://openalex.org/W2251863249","https://openalex.org/W2761847515","https://openalex.org/W130869231","https://openalex.org/W2131689821","https://openalex.org/W2250993361","https://openalex.org/W2921491680"],"abstract_inverted_index":{"Probabilistic":[0],"topic":[1,36,57,102,160],"models,":[2,37],"such":[3],"as":[4,126,128],"latent":[5,79,91],"dirichlet":[6],"allocation":[7],"(LDA),":[8],"are":[9],"often":[10],"used":[11],"to":[12,31,52,121],"discover":[13],"hidden":[14],"semantic":[15],"structure":[16],"of":[17,20,35,56,78,97,100,107,157],"a":[18,44,67,83,98],"collection":[19],"documents.":[21],"In":[22,47],"recent":[23],"years,":[24],"various":[25],"inference":[26,55,87,156],"algorithms":[27],"have":[28,132,144],"been":[29],"developed":[30],"cope":[32],"with":[33],"learning":[34,96],"among":[38],"which":[39,118],"Gibbs":[40,62,71],"sampling":[41,63,72],"methods":[42],"remain":[43],"popular":[45],"choice.":[46],"this":[48],"paper,":[49],"we":[50],"aim":[51],"improve":[53,122],"the":[54,61,76,108,113,142],"models":[58],"based":[59,70],"on":[60,136],"framework.":[64],"We":[65,131],"extend":[66],"state":[68,92],"augmentation":[69],"method":[73,110,149],"by":[74],"maximizing":[75],"replications":[77],"states,":[80],"and":[81,141],"propose":[82],"new":[84],"generic":[85],"deterministic":[86,114],"method,":[88],"named":[89],"maximal":[90],"replication":[93],"(MAX),":[94],"for":[95,116,155],"family":[99],"probabilistic":[101],"models.":[103,161],"One":[104],"key":[105],"benefit":[106],"proposed":[109,148],"lies":[111],"in":[112],"nature":[115],"inference,":[117],"may":[119],"help":[120],"its":[123],"running":[124],"efficiency":[125],"well":[127],"predictive":[129],"perplexity.":[130],"conducted":[133],"extensive":[134],"experiments":[135],"real-life":[137],"publicly":[138],"available":[139],"datasets,":[140],"results":[143],"validated":[145],"that":[146],"our":[147],"MAX":[150],"significantly":[151],"outperforms":[152],"state-of-the-art":[153],"baselines":[154],"existing":[158],"well-known":[159]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
