{"id":"https://openalex.org/W3043400103","doi":"https://doi.org/10.5220/0009874702750282","title":"Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning","display_name":"Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3043400103","doi":"https://doi.org/10.5220/0009874702750282","mag":"3043400103"},"language":"en","primary_location":{"id":"doi:10.5220/0009874702750282","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0009874702750282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Data Science, Technology and Applications","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/A5051937514","display_name":"Fabian Berns","orcid":"https://orcid.org/0000-0002-7033-3789"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Fabian Berns","raw_affiliation_strings":["Department of Computer Science, University of M\u00fcnster, Germany, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of M\u00fcnster, Germany, --- Select a Country ---","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039809923","display_name":"Christian Beecks","orcid":"https://orcid.org/0009-0000-9028-629X"},"institutions":[{"id":"https://openalex.org/I4210110507","display_name":"Fraunhofer Institute for Applied Information Technology","ror":"https://ror.org/01ak24c12","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210110507","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Beecks","raw_affiliation_strings":["Department of Computer Science, University of M\u00fcnster, Germany, Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of M\u00fcnster, Germany, Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany, --- Select a Country ---","institution_ids":["https://openalex.org/I4210110507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051937514"],"corresponding_institution_ids":["https://openalex.org/I22465464"],"apc_list":null,"apc_paid":null,"fwci":0.5412,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73530499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"275","last_page":"282"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9969000220298767,"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":0.9969000220298767,"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":0.9171000123023987,"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":0.7698501348495483},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.722895622253418},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5861141085624695},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5675946474075317},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5391139984130859},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5260108709335327},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5217431783676147},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.2807026505470276}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7698501348495483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722895622253418},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5861141085624695},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5675946474075317},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5391139984130859},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5260108709335327},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5217431783676147},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2807026505470276},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.5220/0009874702750282","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0009874702750282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Data Science, Technology and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:fraunhofer.de:N-614767","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-614767.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer FIT","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/409380","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/409380","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/501487","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/501487","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4224009465","https://openalex.org/W4306674287","https://openalex.org/W4286629047","https://openalex.org/W4205958290","https://openalex.org/W4384212932","https://openalex.org/W4390590544","https://openalex.org/W2096195258","https://openalex.org/W2990460313","https://openalex.org/W2973458857"],"abstract_inverted_index":{"Gaussian":[0,32,41,101],"Process":[1,33],"Models":[2],"(GPMs)":[3],"are":[4,118],"applicable":[5],"for":[6,51,62,67,79,144],"a":[7,31,35,68,91,127,134],"large":[8],"variety":[9],"of":[10,26,100,106,113,115],"different":[11],"data":[12,94,116,164],"analysis":[13],"tasks,":[14],"such":[15],"as":[16,82,168,170],"time":[17],"series":[18],"interpolation,":[19],"regression,":[20],"and":[21,38,138,166,173],"classification.":[22],"Frequently,":[23],"these":[24,44],"models":[25,108],"bayesian":[27],"machine":[28],"learning":[29],"instantiate":[30],"by":[34],"zero-mean":[36],"function":[37],"the":[39,104],"well-known":[40],"kernel.":[42],"While":[43],"default":[45],"instantiations":[46],"yield":[47],"acceptable":[48],"analytical":[49],"quality":[50],"many":[52],"use":[53],"cases,":[54],"GPM":[55,72,130,142,154],"retrieval":[56,73,143],"algorithms":[57,74],"allow":[58],"to":[59,109],"automatically":[60],"search":[61],"an":[63],"application-specific":[64],"model":[65],"suitable":[66],"particular":[69],"dataset.":[70],"State-of-the-art":[71],"have":[75],"only":[76],"been":[77],"applied":[78],"small":[80],"datasets,":[81,111],"their":[83,122],"cubic":[84],"runtime":[85],"complexity":[86],"impedes":[87],"analyzing":[88],"datasets":[89],"beyond":[90,121],"few":[92],"thousand":[93],"records.":[95],"Even":[96],"though":[97],"global":[98],"approximations":[99],"Processes":[102],"extend":[103],"applicability":[105],"those":[107],"medium-sized":[110],"sets":[112],"millions":[114],"records":[117],"still":[119],"far":[120],"reach.":[123],"Therefore,":[124],"we":[125],"develop":[126],"new":[128],"large-scale":[129,145],"structure,":[131],"which":[132],"incorporates":[133],"divide-&-":[135],"conquer-based":[136],"paradigm":[137],"thus":[139],"enables":[140],"efficient":[141],"data.":[146],"We":[147],"outline":[148],"challenges":[149],"concerning":[150],"this":[151],"newly":[152],"developed":[153],"structure":[155],"regarding":[156],"its":[157,160],"algorithmic":[158],"retrieval,":[159],"integration":[161],"with":[162],"given":[163],"platforms":[165],"technologies,":[167],"well":[169],"cross-model":[171],"comparability":[172],"interpretability.":[174]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}