{"id":"https://openalex.org/W2887513810","doi":"https://doi.org/10.18653/v1/p18-5003","title":"Variational Inference and Deep Generative Models","display_name":"Variational Inference and Deep Generative Models","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2887513810","doi":"https://doi.org/10.18653/v1/p18-5003","mag":"2887513810"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-5003","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-5003","pdf_url":"https://www.aclweb.org/anthology/P18-5003.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of ACL 2018, Tutorial Abstracts","raw_type":"proceedings-article"},"type":"conference-abstract","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P18-5003.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074142241","display_name":"Wilker Aziz","orcid":"https://orcid.org/0000-0002-2093-3866"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wilker Aziz","raw_affiliation_strings":["ILLC University of Amsterdam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ILLC University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049524122","display_name":"Philip Schulz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Philip Schulz","raw_affiliation_strings":["Amazon Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.9337000250816345,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.9337000250816345,"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/inference","display_name":"Inference","score":0.7496542930603027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7384039163589478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7021137475967407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6600465774536133},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6584659814834595},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5627883672714233},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5614681839942932},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5375267267227173},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5335230231285095},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5003361701965332},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.49229374527931213},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48301586508750916},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.457547128200531}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7496542930603027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7384039163589478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7021137475967407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6600465774536133},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6584659814834595},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5627883672714233},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5614681839942932},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5375267267227173},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5335230231285095},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5003361701965332},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.49229374527931213},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48301586508750916},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.457547128200531}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p18-5003","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-5003","pdf_url":"https://www.aclweb.org/anthology/P18-5003.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of ACL 2018, Tutorial Abstracts","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-5003","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-5003","pdf_url":"https://www.aclweb.org/anthology/P18-5003.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of ACL 2018, Tutorial Abstracts","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6899999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2887513810.pdf","grobid_xml":"https://content.openalex.org/works/W2887513810.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2988134182","https://openalex.org/W4309969736","https://openalex.org/W2106257677","https://openalex.org/W2964321162","https://openalex.org/W4394785709","https://openalex.org/W4322716735","https://openalex.org/W2806873178","https://openalex.org/W2965146396","https://openalex.org/W2953501176","https://openalex.org/W2770818364"],"abstract_inverted_index":{"Neural":[0],"networks":[1],"are":[2,9],"taking":[3],"NLP":[4,18],"by":[5],"storm.":[6],"Yet":[7],"they":[8,45,54],"mostly":[10],"applied":[11,48],"to":[12,31],"fully":[13],"supervised":[14],"tasks.":[15],"Many":[16],"real-world":[17],"problems":[19],"require":[20],"unsupervised":[21],"or":[22],"semi-supervised":[23],"models,":[24],"however,":[25],"because":[26],"annotated":[27,61],"data":[28,51,62],"is":[29,34],"hard":[30],"obtain.":[32],"This":[33],"where":[35],"generative":[36],"models":[37],"shine.":[38],"Through":[39],"the":[40],"use":[41],"of":[42],"latent":[43],"variables":[44],"can":[46,55],"be":[47],"in":[49,59],"missing":[50,57],"settings.":[52],"Furthermore":[53],"complete":[56],"entries":[58],"partially":[60],"sets.":[63]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
