{"id":"https://openalex.org/W2937866564","doi":"https://doi.org/10.1109/icassp.2019.8683771","title":"Variational and Hierarchical Recurrent Autoencoder","display_name":"Variational and Hierarchical Recurrent Autoencoder","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2937866564","doi":"https://doi.org/10.1109/icassp.2019.8683771","mag":"2937866564"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8683771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061908942","display_name":"Jen\u2010Tzung Chien","orcid":"https://orcid.org/0000-0003-3466-8941"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jen-Tzung Chien","raw_affiliation_strings":["Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059684490","display_name":"Chunwei Wang","orcid":"https://orcid.org/0000-0001-8093-9058"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chun-Wei Wang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3202","last_page":"3206"},"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":0.9915000200271606,"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":0.9915000200271606,"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/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9904000163078308,"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/autoencoder","display_name":"Autoencoder","score":0.8501269817352295},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.710818886756897},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.7003501057624817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6287805438041687},{"id":"https://openalex.org/keywords/treebank","display_name":"Treebank","score":0.6271764039993286},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5611741542816162},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5285331606864929},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4807531535625458},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.47318220138549805},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4730754792690277},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.436423122882843},{"id":"https://openalex.org/keywords/sequence-learning","display_name":"Sequence learning","score":0.4351186752319336},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42276573181152344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3997052311897278},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38271716237068176},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3596441447734833},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3563368618488312}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8501269817352295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.710818886756897},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.7003501057624817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6287805438041687},{"id":"https://openalex.org/C206134035","wikidata":"https://www.wikidata.org/wiki/Q811525","display_name":"Treebank","level":3,"score":0.6271764039993286},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5611741542816162},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5285331606864929},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4807531535625458},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.47318220138549805},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4730754792690277},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.436423122882843},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.4351186752319336},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42276573181152344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3997052311897278},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38271716237068176},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3596441447734833},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3563368618488312},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8683771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1933340426","https://openalex.org/W1959608418","https://openalex.org/W1999965501","https://openalex.org/W2056985955","https://openalex.org/W2099471712","https://openalex.org/W2173681125","https://openalex.org/W2242047860","https://openalex.org/W2267126114","https://openalex.org/W2327501763","https://openalex.org/W2405331948","https://openalex.org/W2519091744","https://openalex.org/W2549476280","https://openalex.org/W2560512785","https://openalex.org/W2572816092","https://openalex.org/W2586756136","https://openalex.org/W2606176153","https://openalex.org/W2747695703","https://openalex.org/W2749679082","https://openalex.org/W2752796333","https://openalex.org/W2796335507","https://openalex.org/W2889008527","https://openalex.org/W2898944992","https://openalex.org/W2938799435","https://openalex.org/W2949382160","https://openalex.org/W2953046278","https://openalex.org/W2953318193","https://openalex.org/W2963081964","https://openalex.org/W2963135265","https://openalex.org/W2963223306","https://openalex.org/W2963319343","https://openalex.org/W2963600562","https://openalex.org/W2963773425","https://openalex.org/W2963799213","https://openalex.org/W2963987720","https://openalex.org/W2964168257","https://openalex.org/W2964339599","https://openalex.org/W4293411878","https://openalex.org/W4294107275","https://openalex.org/W4295177495","https://openalex.org/W4297733427","https://openalex.org/W4297940714","https://openalex.org/W4320013936","https://openalex.org/W6640284076","https://openalex.org/W6640963894","https://openalex.org/W6685356407","https://openalex.org/W6729349781","https://openalex.org/W6730998768","https://openalex.org/W6731535438","https://openalex.org/W6734716764","https://openalex.org/W6736723571","https://openalex.org/W6738623590","https://openalex.org/W6743827229","https://openalex.org/W6747888389","https://openalex.org/W6749820799","https://openalex.org/W6753417141","https://openalex.org/W6754395538"],"related_works":["https://openalex.org/W2376415519","https://openalex.org/W2740662036","https://openalex.org/W1601381279","https://openalex.org/W2739034105","https://openalex.org/W4301800915","https://openalex.org/W2963839582","https://openalex.org/W2964047924","https://openalex.org/W2743945814","https://openalex.org/W4299838440","https://openalex.org/W2962832505"],"abstract_inverted_index":{"Despite":[0],"a":[1,64,87,103,109,124],"great":[2],"success":[3],"in":[4,28,61,123,157,169],"learning":[5,31,62],"representation":[6,149],"for":[7,81,93],"image":[8],"data,":[9],"it":[10],"is":[11,32,44,68,143,166],"challenging":[12],"to":[13,34,46,50,70,116,145],"learn":[14,51,146],"the":[15,35,118,139,147,154],"stochastic":[16,29,158],"latent":[17,53,66,126],"features":[18,77],"from":[19],"natural":[20],"language":[21],"based":[22],"on":[23,131],"variational":[24,89],"inference.":[25],"The":[26,161],"difficulty":[27],"sequential":[30,82,159],"due":[33],"posterior":[36,155],"collapse":[37,156],"caused":[38],"by":[39],"an":[40],"autoregressive":[41],"decoder":[42],"which":[43,113],"prone":[45],"be":[47],"too":[48],"strong":[49],"sufficient":[52],"information":[54],"during":[55],"optimization.":[56],"To":[57],"compensate":[58],"this":[59],"weakness":[60],"procedure,":[63],"sophisticated":[65],"structure":[67],"required":[69],"assure":[71],"good":[72],"convergence":[73],"so":[74],"that":[75,138],"random":[76],"are":[78,97,114],"sufficiently":[79],"captured":[80],"decoding.":[83],"This":[84],"study":[85],"presents":[86],"new":[88],"recurrent":[90,164],"autoencoder":[91,165],"(VRAE)":[92],"sequence":[94],"reconstruction.":[95],"There":[96],"two":[98],"complementary":[99,148],"encoders":[100],"consisting":[101],"of":[102,163,171],"long":[104],"short-term":[105],"memory":[106],"(LSTM)":[107],"and":[108,120,134],"pyramid":[110],"bidirectional":[111],"LSTM":[112],"merged":[115],"discover":[117],"global":[119],"local":[121],"dependencies":[122],"hierarchical":[125,141],"variable":[127],"model,":[128],"respectively.":[129],"Experiments":[130],"Penn":[132],"Treebank":[133],"Yelp":[135],"2013":[136],"demonstrate":[137],"proposed":[140],"VRAE":[142],"able":[144],"as":[150,152],"well":[151],"tackle":[153],"learning.":[160],"performance":[162],"substantially":[167],"improved":[168],"terms":[170],"perplexity.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}