{"id":"https://openalex.org/W4393188566","doi":"https://doi.org/10.1109/tpami.2024.3381936","title":"Gaussian Process-Gated Hierarchical Mixtures of Experts","display_name":"Gaussian Process-Gated Hierarchical Mixtures of Experts","publication_year":2024,"publication_date":"2024-03-26","ids":{"openalex":"https://openalex.org/W4393188566","doi":"https://doi.org/10.1109/tpami.2024.3381936","pmid":"https://pubmed.ncbi.nlm.nih.gov/38530736"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2024.3381936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3381936","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_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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100366624","display_name":"Yuhao Liu","orcid":"https://orcid.org/0000-0001-9636-747X"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuhao Liu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022639421","display_name":"Marzieh Ajirak","orcid":"https://orcid.org/0000-0003-4613-5018"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marzieh Ajirak","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuri\u0107","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100366624"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":1.0425,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.79035543,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"46","issue":"9","first_page":"6443","last_page":"6453"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":1.0,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":1.0,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9757999777793884,"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/T10320","display_name":"Neural Networks and Applications","score":0.9660999774932861,"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/gaussian-process","display_name":"Gaussian process","score":0.7462193369865417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7052881121635437},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6856533288955688},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5669494867324829},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5422372221946716},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4683755338191986},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.41103029251098633},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.41068220138549805},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.389897882938385},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3865357041358948},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3788755536079407}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7462193369865417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7052881121635437},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6856533288955688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5669494867324829},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5422372221946716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4683755338191986},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.41103029251098633},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.41068220138549805},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.389897882938385},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3865357041358948},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3788755536079407},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2024.3381936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3381936","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_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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:38530736","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38530736","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_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":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W854322902","https://openalex.org/W1567512734","https://openalex.org/W1571870753","https://openalex.org/W1665378886","https://openalex.org/W1982525872","https://openalex.org/W1983957710","https://openalex.org/W2016023958","https://openalex.org/W2025653905","https://openalex.org/W2066350599","https://openalex.org/W2092271904","https://openalex.org/W2125055259","https://openalex.org/W2131919261","https://openalex.org/W2145649595","https://openalex.org/W2156163116","https://openalex.org/W2532700006","https://openalex.org/W2766678531","https://openalex.org/W2911845017","https://openalex.org/W2951539267","https://openalex.org/W3103364054","https://openalex.org/W3161622520","https://openalex.org/W4293579573","https://openalex.org/W4297573784","https://openalex.org/W4297821991","https://openalex.org/W4387008033","https://openalex.org/W6604527594","https://openalex.org/W6631732945","https://openalex.org/W6639724578","https://openalex.org/W6677317343","https://openalex.org/W6681302627","https://openalex.org/W6684918892","https://openalex.org/W6686366921","https://openalex.org/W6728725681","https://openalex.org/W6729403857","https://openalex.org/W6740718799","https://openalex.org/W6745256532","https://openalex.org/W6745861399","https://openalex.org/W6773555910","https://openalex.org/W6790939575","https://openalex.org/W6795164119","https://openalex.org/W6796080340","https://openalex.org/W6803484255"],"related_works":["https://openalex.org/W4206864338","https://openalex.org/W3104422856","https://openalex.org/W4287867179","https://openalex.org/W3134690064","https://openalex.org/W4210726438","https://openalex.org/W4287752080","https://openalex.org/W3118984993","https://openalex.org/W3037706579","https://openalex.org/W4401389295","https://openalex.org/W3157580548"],"abstract_inverted_index":{"In":[0],"this":[1],"article,":[2],"we":[3],"propose":[4],"novel":[5],"Gaussian":[6,32],"process-gated":[7],"hierarchical":[8],"mixtures":[9,15],"of":[10,16,46,62],"experts":[11,17,51],"(GPHMEs).":[12],"Unlike":[13],"other":[14],"with":[18,31,58,94,125],"gating":[19,28],"models":[20],"linear":[21],"in":[22,52,85],"the":[23,47,50,63,83,86,100],"input,":[24],"our":[25,53],"model":[26,54],"employs":[27],"functions":[29,45],"built":[30],"processes":[33,36],"(GPs).":[34],"These":[35],"are":[37,43,55],"based":[38],"on":[39],"random":[40],"features":[41],"that":[42,81],"non-linear":[44],"inputs.":[48],"Furthermore,":[49],"also":[56],"constructed":[57],"GPs.":[59],"The":[60,70],"optimization":[61],"GPHMEs":[64,72,116],"is":[65,99],"performed":[66],"by":[67],"variational":[68],"inference.":[69],"proposed":[71],"have":[73],"several":[74],"advantages.":[75],"They":[76],"outperform":[77],"tree-based":[78],"HME":[79],"benchmarks":[80],"partition":[82],"data":[84,122],"input":[87],"space,":[88],"and":[89,107],"they":[90,102],"achieve":[91],"good":[92],"performance":[93,119],"reduced":[95],"complexity.":[96],"Another":[97],"advantage":[98],"interpretability":[101],"provide":[103],"for":[104,110,120],"deep":[105,111],"GPs,":[106],"more":[108],"generally,":[109],"Bayesian":[112],"neural":[113],"networks.":[114],"Our":[115],"demonstrate":[117],"excellent":[118],"large-scale":[121],"sets,":[123],"even":[124],"quite":[126],"modest":[127],"sizes.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}