{"id":"https://openalex.org/W3009517571","doi":"https://doi.org/10.1109/lra.2020.3010206","title":"q-VAE for Disentangled Representation Learning and Latent Dynamical Systems","display_name":"q-VAE for Disentangled Representation Learning and Latent Dynamical Systems","publication_year":2020,"publication_date":"2020-07-17","ids":{"openalex":"https://openalex.org/W3009517571","doi":"https://doi.org/10.1109/lra.2020.3010206","mag":"3009517571"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2020.3010206","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2020.3010206","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.01852","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Taisuke Kobayashis","orcid":"https://orcid.org/0000-0002-3760-249X"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Taisuke Kobayashis","raw_affiliation_strings":["Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan"],"affiliations":[{"raw_affiliation_string":"Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan","institution_ids":["https://openalex.org/I75917431"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75917431"],"apc_list":null,"apc_paid":null,"fwci":0.6868,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.71118684,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"5","issue":"4","first_page":"5669","last_page":"5676"},"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":0.3089999854564667,"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":0.3089999854564667,"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":0.2223999947309494,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.06300000101327896,"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/mnist-database","display_name":"MNIST database","score":0.7483999729156494},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6747999787330627},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5931000113487244},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5315999984741211},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.41990000009536743},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.41269999742507935},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3952000141143799},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.38190001249313354}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7483999729156494},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6747999787330627},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5931000113487244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5577999949455261},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4758000075817108},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40529999136924744},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40470001101493835},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35040000081062317},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C52918065","wikidata":"https://www.wikidata.org/wiki/Q230945","display_name":"State-space representation","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lra.2020.3010206","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2020.3010206","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2003.01852","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.01852","pdf_url":"https://arxiv.org/pdf/2003.01852","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.01852","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.01852","pdf_url":"https://arxiv.org/pdf/2003.01852","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1979985652","https://openalex.org/W1983874169","https://openalex.org/W2059374347","https://openalex.org/W2095669515","https://openalex.org/W2198582666","https://openalex.org/W2199578125","https://openalex.org/W2418368699","https://openalex.org/W2623473014","https://openalex.org/W2790919587","https://openalex.org/W2808266363","https://openalex.org/W2900582619","https://openalex.org/W2919115771","https://openalex.org/W2946896833","https://openalex.org/W2950878834","https://openalex.org/W2962834855","https://openalex.org/W4239272157","https://openalex.org/W6623316541","https://openalex.org/W6628782201","https://openalex.org/W6640963894","https://openalex.org/W6680097440","https://openalex.org/W6711952718","https://openalex.org/W6744063608","https://openalex.org/W6744627333","https://openalex.org/W6746023985","https://openalex.org/W6748223763","https://openalex.org/W6748391871","https://openalex.org/W6750852989","https://openalex.org/W6754302822","https://openalex.org/W6756040250","https://openalex.org/W6756256016","https://openalex.org/W6756824971","https://openalex.org/W6773784147","https://openalex.org/W6780226713","https://openalex.org/W6780559895"],"related_works":[],"abstract_inverted_index":{"A":[0],"variational":[1],"autoencoder":[2],"(VAE)":[3],"derived":[4,82],"from":[5,68,118,177],"Tsallis":[6,70],"statistics":[7,71],"called":[8],"q-VAE":[9,80,130,169],"is":[10,19,62,81,152],"proposed.":[11],"In":[12,142],"the":[13,46,49,63,78,85,88,104,107,115,119,128,136,140,146,156,167,178,182],"proposed":[14,79,108,129,168],"method,":[15],"a":[16,69,73,110],"standard":[17],"VAE":[18],"employed":[20],"to":[21,83,113],"statistically":[22],"extract":[23,114],"latent":[24,32,50,116,157],"space":[25,33,117],"hidden":[26],"in":[27,38],"sampled":[28,89],"data,":[29,90,150],"and":[30,42,172,181],"this":[31,52],"helps":[34],"make":[35],"robots":[36],"controllable":[37],"feasible":[39],"computational":[40],"time":[41],"cost.":[43],"To":[44,102],"improve":[45],"usefulness":[47],"of":[48,87,106,139,159],"space,":[51],"letter":[53],"focuses":[54],"on":[55],"disentangled":[56,132,165],"representation":[57,133],"learning,":[58],"e.g.,":[59],"\u03b2-VAE,":[60],"which":[61,91,151],"baseline":[64],"for":[65,77],"it.":[66],"Starting":[67],"perspective,":[72],"new":[74],"lower":[75],"bound":[76],"maximize":[84],"likelihood":[86],"can":[92],"be":[93],"considered":[94],"an":[95],"adaptive":[96],"\u03b2-VAE":[97],"with":[98],"deformed":[99],"Kullback-Leibler":[100],"divergence.":[101],"verify":[103],"benefits":[105],"q-VAE,":[109],"benchmark":[111],"task":[112],"MNIST":[120],"dataset":[121],"was":[122],"performed.":[123],"The":[124],"results":[125],"demonstrate":[126],"that":[127],"improved":[131],"while":[134],"maintaining":[135],"reconstruction":[137],"accuracy":[138],"data.":[141],"addition,":[143],"it":[144],"relaxes":[145],"independency":[147],"condition":[148],"between":[149],"demonstrated":[153],"by":[154],"learning":[155],"dynamics":[158],"nonlinear":[160],"dynamical":[161],"systems.":[162],"By":[163],"combining":[164],"representation,":[166],"achieves":[170],"stable":[171],"accurate":[173],"long-term":[174],"state":[175,180],"prediction":[176],"initial":[179],"action":[183],"sequence.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2020-03-13T00:00:00"}
