{"id":"https://openalex.org/W4401359677","doi":"https://doi.org/10.1007/s11222-024-10481-x","title":"Learning variational autoencoders via MCMC speed measures","display_name":"Learning variational autoencoders via MCMC speed measures","publication_year":2024,"publication_date":"2024-08-06","ids":{"openalex":"https://openalex.org/W4401359677","doi":"https://doi.org/10.1007/s11222-024-10481-x"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-024-10481-x","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s11222-024-10481-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-024-10481-x.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-024-10481-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028431074","display_name":"Marcel Hirt","orcid":null},"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":true,"raw_author_name":"Marcel Hirt","raw_affiliation_strings":["School of Social Sciences and School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Social Sciences and School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041566354","display_name":"Vasileios Kreouzis","orcid":"https://orcid.org/0000-0002-8207-9933"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vasileios Kreouzis","raw_affiliation_strings":["Department of Statistical Science, University College London, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017494868","display_name":"\u03a0\u03ad\u03c4\u03c1\u03bf\u03c2 \u0394\u03b5\u03bb\u03bb\u03b1\u03c0\u03cc\u03c1\u03c4\u03b1\u03c2","orcid":"https://orcid.org/0000-0002-0117-8447"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]},{"id":"https://openalex.org/I73142707","display_name":"Athens University of Economics and Business","ror":"https://ror.org/03s262162","country_code":"GR","type":"education","lineage":["https://openalex.org/I73142707"]}],"countries":["GB","GR"],"is_corresponding":false,"raw_author_name":"Petros Dellaportas","raw_affiliation_strings":["Department of Statistical Science, University College London, London, UK","Department of Statistics, Athens University of Economics and Business, Athina, Greece"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"Department of Statistics, Athens University of Economics and Business, Athina, Greece","institution_ids":["https://openalex.org/I73142707"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028431074"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":0.5263,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64711891,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"34","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9702000021934509,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8330920934677124},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6561824679374695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5498887300491333},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5105029344558716},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5014772415161133},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.4912511706352234},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4867539405822754},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4791521430015564},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.44751617312431335},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44150063395500183},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4358224868774414},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.432017058134079},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.423613578081131},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.42358750104904175},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3861524760723114},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.38587626814842224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29443585872650146},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.15084117650985718},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13282370567321777}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8330920934677124},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6561824679374695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5498887300491333},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5105029344558716},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5014772415161133},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.4912511706352234},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4867539405822754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4791521430015564},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.44751617312431335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44150063395500183},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4358224868774414},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.432017058134079},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.423613578081131},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.42358750104904175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3861524760723114},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38587626814842224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29443585872650146},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.15084117650985718},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13282370567321777},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11222-024-10481-x","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s11222-024-10481-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-024-10481-x.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11222-024-10481-x","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s11222-024-10481-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-024-10481-x.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401359677.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W174941419","https://openalex.org/W1501586228","https://openalex.org/W1522579744","https://openalex.org/W1981276685","https://openalex.org/W1997628954","https://openalex.org/W2013035813","https://openalex.org/W2125027820","https://openalex.org/W2142274086","https://openalex.org/W2169713291","https://openalex.org/W2519766107","https://openalex.org/W2730106296","https://openalex.org/W2768877132","https://openalex.org/W2783417494","https://openalex.org/W2899379230","https://openalex.org/W2913002991","https://openalex.org/W2914584698","https://openalex.org/W2972588190","https://openalex.org/W3104101668","https://openalex.org/W3107912273","https://openalex.org/W3132017167","https://openalex.org/W3199585919","https://openalex.org/W3209501356","https://openalex.org/W3212280254","https://openalex.org/W4312933868","https://openalex.org/W4390100567","https://openalex.org/W4390873596","https://openalex.org/W6600002382","https://openalex.org/W6600013530","https://openalex.org/W6600540670","https://openalex.org/W6603593944","https://openalex.org/W6603703757","https://openalex.org/W6610028639","https://openalex.org/W6813542777","https://openalex.org/W6848060309"],"related_works":["https://openalex.org/W2010982255","https://openalex.org/W2042221696","https://openalex.org/W2616125534","https://openalex.org/W2614538623","https://openalex.org/W2963987720","https://openalex.org/W3022187094","https://openalex.org/W2146310005","https://openalex.org/W2963790827","https://openalex.org/W2786010873","https://openalex.org/W4293659211"],"abstract_inverted_index":{"Abstract":[0],"Variational":[1],"autoencoders":[2],"(VAEs)":[3],"are":[4],"popular":[5],"likelihood-based":[6],"generative":[7,40,111],"models":[8,67],"which":[9],"can":[10,117],"be":[11],"efficiently":[12],"trained":[13],"by":[14],"maximising":[15],"an":[16,75],"evidence":[17],"lower":[18],"bound.":[19],"There":[20],"has":[21,45],"been":[22],"much":[23],"progress":[24],"in":[25,128],"improving":[26],"the":[27,30,60,96],"expressiveness":[28],"of":[29,123],"variational":[31,36,54,93,115],"distribution":[32],"to":[33,95,119],"obtain":[34],"tighter":[35,92],"bounds":[37],"and":[38],"increased":[39],"performance.":[41],"Whilst":[42],"previous":[43],"work":[44,73],"leveraged":[46],"Markov":[47],"chain":[48,88],"Monte":[49,85],"Carlo":[50,86],"methods":[51,57],"for":[52,58,63,78],"constructing":[53],"densities,":[55],"gradient-based":[56],"adapting":[59],"proposal":[61],"distributions":[62],"deep":[64],"latent":[65,124],"variable":[66],"have":[68],"received":[69],"less":[70],"attention.":[71],"This":[72],"suggests":[74],"entropy-based":[76],"adaptation":[77],"a":[79,91],"short-run":[80],"metropolis-adjusted":[81],"Langevin":[82],"or":[83],"Hamiltonian":[84],"(HMC)":[87],"while":[89],"optimising":[90],"bound":[94],"log-evidence.":[97],"Experiments":[98],"show":[99],"that":[100],"this":[101],"approach":[102],"yields":[103],"higher":[104],"held-out":[105],"log-likelihoods":[106],"as":[107,109],"well":[108],"improved":[110],"metrics.":[112],"Our":[113],"implicit":[114],"density":[116],"adapt":[118],"complicated":[120],"posterior":[121],"geometries":[122],"hierarchical":[125,129],"representations":[126],"arising":[127],"VAEs.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}