{"id":"https://openalex.org/W4416692422","doi":"https://doi.org/10.14428/esann/2026.es2026-167","title":"Distillation of a tractable model from the VQ-VAE","display_name":"Distillation of a tractable model from the VQ-VAE","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4416692422","doi":"https://doi.org/10.14428/esann/2026.es2026-167"},"language":"en","primary_location":{"id":"doi:10.14428/esann/2026.es2026-167","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2026.es2026-167","pdf_url":"https://doi.org/10.14428/esann/2026.es2026-167","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2026 proceesdings","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.14428/esann/2026.es2026-167","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120475371","display_name":"Armin Had\u017ei\u0107","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Armin Hadzic","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050267602","display_name":"Milan Pape\u017e","orcid":"https://orcid.org/0000-0002-6700-081X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milan Pape\u017e","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059337672","display_name":"Tom\u00e1\u0161 Pevn\u00fd","orcid":"https://orcid.org/0000-0002-5768-9713"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tom\u00e1\u0161 Pevn\u00fd","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02322036,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"745","last_page":"750"},"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":0.9753999710083008,"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":0.9753999710083008,"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/T11448","display_name":"Face recognition and analysis","score":0.004600000102072954,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0024999999441206455,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7174999713897705},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6322000026702881},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6004999876022339},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5523999929428101},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.545799970626831},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.48510000109672546},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.47600001096725464},{"id":"https://openalex.org/keywords/probabilistic-relevance-model","display_name":"Probabilistic relevance model","score":0.4650000035762787}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7174999713897705},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6322000026702881},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6004999876022339},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5523999929428101},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5358999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51419997215271},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C143017306","wikidata":"https://www.wikidata.org/wiki/Q3318133","display_name":"Probabilistic relevance model","level":4,"score":0.4650000035762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4546999931335449},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3865000009536743},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32839998602867126},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3231000006198883},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.25600001215934753}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.14428/esann/2026.es2026-167","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2026.es2026-167","pdf_url":"https://doi.org/10.14428/esann/2026.es2026-167","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2026 proceesdings","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.01400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01400","pdf_url":"https://arxiv.org/pdf/2509.01400","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2509.01400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.01400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.14428/esann/2026.es2026-167","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2026.es2026-167","pdf_url":"https://doi.org/10.14428/esann/2026.es2026-167","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2026 proceesdings","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2291329386","display_name":null,"funder_award_id":"CZ.02.1.01/0.0/0.0/16_019/0000765","funder_id":"https://openalex.org/F4320334253","funder_display_name":"Research Center for Informatics, Czech Technical University in Prague"},{"id":"https://openalex.org/G8470341104","display_name":null,"funder_award_id":"CZ.02.1.01/0.0/0.0/16_019/","funder_id":"https://openalex.org/F4320334253","funder_display_name":"Research Center for Informatics, Czech Technical University in Prague"}],"funders":[{"id":"https://openalex.org/F4320334253","display_name":"Research Center for Informatics, Czech Technical University in Prague","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416692422.pdf","grobid_xml":"https://content.openalex.org/works/W4416692422.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"generative":[1],"models":[2],"with":[3,52],"a":[4,42,47,63],"discrete":[5],"latent":[6,27,50],"space,":[7],"such":[8],"as":[9,62,98],"the":[10,22,36,56,59,71,74,93,96],"Vector-Quantized":[11],"Variational":[12],"Autoencoder":[13],"(VQ-VAE),":[14],"offer":[15],"excellent":[16],"data":[17],"generation":[18,90],"capabilities,":[19],"but-due":[20],"to":[21],"large":[23],"size":[24],"of":[25,49,73,95],"their":[26],"space-their":[28],"probabilistic":[29,64,79],"inference":[30],"is":[31],"deemed":[32],"intractable.We":[33],"demonstrate":[34],"that":[35,68],"VQ-VAE":[37,75,97],"can":[38],"be":[39],"distilled":[40,60],"into":[41],"tractable":[43,78],"model":[44,61],"by":[45],"selecting":[46],"subset":[48],"variables":[51],"high":[53],"probability":[54],"under":[55],"prior.We":[57],"frame":[58],"circuit,":[65],"and":[66,88],"show":[67],"it":[69],"preserves":[70],"expressiveness":[72],"while":[76],"providing":[77],"inference.Experiments":[80],"illustrate":[81],"competitive":[82],"performance":[83],"in":[84],"both":[85],"density":[86],"estimation":[87],"conditional":[89],"tasks,":[91],"challenging":[92],"view":[94],"an":[99],"inherently":[100],"intractable":[101],"model.":[102]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
