{"id":"https://openalex.org/W4225613970","doi":"https://doi.org/10.1109/icmla52953.2021.00138","title":"Regularized Sequential Latent Variable Models with Adversarial Neural Networks","display_name":"Regularized Sequential Latent Variable Models with Adversarial Neural Networks","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4225613970","doi":"https://doi.org/10.1109/icmla52953.2021.00138"},"language":"en","primary_location":{"id":"doi:10.1109/icmla52953.2021.00138","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla52953.2021.00138","pdf_url":null,"source":{"id":"https://openalex.org/S4363607906","display_name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","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/A5087389386","display_name":"Jin Huang","orcid":"https://orcid.org/0000-0001-8774-2936"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Jin Huang","raw_affiliation_strings":["KTH Royal Institute of Technology,the Division of Information Science and Engineering,Stockholm,Sweden","the Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,the Division of Information Science and Engineering,Stockholm,Sweden","institution_ids":["https://openalex.org/I86987016"]},{"raw_affiliation_string":"the Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037292846","display_name":"Ming Xiao","orcid":"https://orcid.org/0000-0002-5407-0835"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ming Xiao","raw_affiliation_strings":["KTH Royal Institute of Technology,the Division of Information Science and Engineering,Stockholm,Sweden","the Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,the Division of Information Science and Engineering,Stockholm,Sweden","institution_ids":["https://openalex.org/I86987016"]},{"raw_affiliation_string":"the Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087389386"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":0.196,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60887097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"31","issue":null,"first_page":"834","last_page":"839"},"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":0.9976000189781189,"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":0.9976000189781189,"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/T10201","display_name":"Speech Recognition and Synthesis","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9965999722480774,"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/latent-variable","display_name":"Latent variable","score":0.7581760883331299},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6872715950012207},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.678777277469635},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.600879967212677},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.5804070830345154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5374579429626465},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5044509172439575},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49075350165367126},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4825281798839569},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.47813642024993896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46035003662109375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40471768379211426},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.21279111504554749}],"concepts":[{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.7581760883331299},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6872715950012207},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.678777277469635},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.600879967212677},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.5804070830345154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5374579429626465},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5044509172439575},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49075350165367126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4825281798839569},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.47813642024993896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46035003662109375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40471768379211426},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.21279111504554749}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla52953.2021.00138","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla52953.2021.00138","pdf_url":null,"source":{"id":"https://openalex.org/S4363607906","display_name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W592244745","https://openalex.org/W1779483307","https://openalex.org/W1884859883","https://openalex.org/W1959608418","https://openalex.org/W2064675550","https://openalex.org/W2125389028","https://openalex.org/W2157331557","https://openalex.org/W2396566817","https://openalex.org/W2470142083","https://openalex.org/W2554923929","https://openalex.org/W2951326654","https://openalex.org/W2952673310","https://openalex.org/W2963373786","https://openalex.org/W2963684088","https://openalex.org/W2964232608","https://openalex.org/W4206566734","https://openalex.org/W4293568373","https://openalex.org/W4295177495","https://openalex.org/W4297733427","https://openalex.org/W4300532011","https://openalex.org/W4320013936","https://openalex.org/W6617744952","https://openalex.org/W6638116569","https://openalex.org/W6639735774","https://openalex.org/W6640963894","https://openalex.org/W6678815747","https://openalex.org/W6685352114","https://openalex.org/W6712395597","https://openalex.org/W6718379498","https://openalex.org/W6727654133","https://openalex.org/W6732943021","https://openalex.org/W6738623590","https://openalex.org/W6761061368","https://openalex.org/W6779669310","https://openalex.org/W6780248173"],"related_works":["https://openalex.org/W80423236","https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2988134182","https://openalex.org/W4298148390","https://openalex.org/W80018097","https://openalex.org/W2964207879","https://openalex.org/W2100729928","https://openalex.org/W3181208372"],"abstract_inverted_index":{"The":[0],"highly":[1],"structured":[2],"sequential":[3,45],"data,":[4],"such":[5,55],"as":[6],"from":[7,123],"speech":[8,125],"and":[9,20,96,131,140],"handwriting,":[10],"often":[11],"contain":[12],"complex":[13],"relationships":[14],"between":[15],"the":[16,21,41,44,50,59,74,79,93,106,110,137],"underlaying":[17],"variational":[18,80,111],"factors":[19],"observed":[22],"data.":[23,46],"This":[24],"paper":[25],"will":[26],"present":[27],"different":[28],"ways":[29],"of":[30,54,68],"using":[31,69],"high":[32],"level":[33,139],"latent":[34,75],"random":[35],"variables":[36],"in":[37,43,78,92,109],"RNN":[38,56,81],"to":[39,72,84,136,145],"model":[40,57,94,99],"variability":[42],"We":[47,64],"have":[48],"developed":[49],"two-steps":[51],"training":[52,71,95,100,118,142],"algorithms":[53],"under":[58],"VAE":[60],"(Variational":[61],"Autoencoder)":[62],"principle.":[63],"proposed":[65],"novel":[66],"approach":[67,88,103],"adversarial":[70,117,141],"regularize":[73],"variable":[76],"distributions":[77],"model.":[82],"Contrary":[83],"competing":[85],"approaches,":[86],"our":[87],"has":[89],"theoretical":[90],"optimum":[91],"provides":[97],"better":[98],"stability.":[101],"Our":[102],"also":[104],"improves":[105],"posterior":[107],"approximation":[108],"inference":[112],"network":[113],"by":[114],"a":[115],"separated":[116],"step.":[119],"Numerical":[120],"results":[121],"simulated":[122],"TIMIT":[124],"data":[126],"show":[127],"that":[128],"reconstruction":[129],"loss":[130,143],"evidence":[132],"lower":[133],"bound":[134],"converge":[135],"same":[138],"converges":[144],"0.":[146]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}