{"id":"https://openalex.org/W2898944992","doi":"https://doi.org/10.1109/mlsp.2018.8517074","title":"MARKOV RECURRENT NEURAL NETWORKS","display_name":"MARKOV RECURRENT NEURAL NETWORKS","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2898944992","doi":"https://doi.org/10.1109/mlsp.2018.8517074","mag":"2898944992"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2018.8517074","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2018.8517074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5032182651","display_name":"Che-Yu Kuo","orcid":null},"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":"Che-Yu Kuo","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/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"]}]}],"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":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9941999912261963,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9911999702453613,"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/computer-science","display_name":"Computer science","score":0.7759526968002319},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7338914275169373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6563003659248352},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.545133650302887},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5156070590019226},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5086466073989868},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.49793457984924316},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48685187101364136},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4789079427719116},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.458683043718338},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4508282542228699},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4263339638710022},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4246987998485565},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.38346001505851746},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10249939560890198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7759526968002319},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7338914275169373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6563003659248352},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.545133650302887},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5156070590019226},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5086466073989868},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.49793457984924316},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48685187101364136},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4789079427719116},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.458683043718338},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4508282542228699},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4263339638710022},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4246987998485565},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.38346001505851746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10249939560890198},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2018.8517074","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2018.8517074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W592244745","https://openalex.org/W1591801644","https://openalex.org/W1884859883","https://openalex.org/W1959608418","https://openalex.org/W1999965501","https://openalex.org/W2056985955","https://openalex.org/W2119717200","https://openalex.org/W2185758820","https://openalex.org/W2212703438","https://openalex.org/W2242047860","https://openalex.org/W2396566817","https://openalex.org/W2405331948","https://openalex.org/W2547875792","https://openalex.org/W2548228487","https://openalex.org/W2592696501","https://openalex.org/W2962824919","https://openalex.org/W2963266340","https://openalex.org/W2963279312","https://openalex.org/W2963524109","https://openalex.org/W2963790827","https://openalex.org/W2964076986","https://openalex.org/W3022187094","https://openalex.org/W4230563027","https://openalex.org/W4300802867","https://openalex.org/W6617744952","https://openalex.org/W6635446068","https://openalex.org/W6639735774","https://openalex.org/W6640963894","https://openalex.org/W6686368241","https://openalex.org/W6688167117","https://openalex.org/W6712395597","https://openalex.org/W6712878229","https://openalex.org/W6729448088","https://openalex.org/W6730091202","https://openalex.org/W6738539399","https://openalex.org/W6743300111"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2194396582","https://openalex.org/W2082284720","https://openalex.org/W2116722627","https://openalex.org/W2537260108","https://openalex.org/W2379938888","https://openalex.org/W4233405330","https://openalex.org/W2792905593","https://openalex.org/W1977445474"],"abstract_inverted_index":{"Deep":[0],"learning":[1,58,131,149],"has":[2],"achieved":[3],"great":[4],"success":[5],"in":[6,70,93,111,136,170],"many":[7],"real-world":[8],"applications.":[9],"For":[10],"speech":[11],"and":[12,24,38,50,73,139,158],"language":[13,160],"processing,":[14],"recurrent":[15],"neural":[16],"networks":[17],"are":[18,34],"learned":[19],"to":[20,63,81,100,146],"characterize":[21],"sequential":[22,71,83,95,112,141,171],"patterns":[23],"extract":[25],"the":[26,57,65,75,88,107,132,163,166],"temporal":[27],"information":[28],"based":[29,114],"on":[30,115,156],"dynamic":[31],"states":[32],"which":[33],"evolved":[35],"through":[36],"time":[37],"stored":[39],"as":[40],"an":[41],"internal":[42],"memory.":[43],"Traditionally,":[44],"simple":[45],"transition":[46],"function":[47],"using":[48],"input-to-hidden":[49],"hidden-to-hidden":[51],"weights":[52],"is":[53,61,99,128,144],"insufficient.":[54],"To":[55],"strengthen":[56],"capability,":[59],"it":[60],"crucial":[62],"explore":[64],"diversity":[66],"of":[67,78,91,130,165,176],"latent":[68,102,125,134],"structure":[69],"signals":[72],"learn":[74],"stochastic":[76,89,148,168],"trajectory":[77],"signal":[79],"transitions":[80,92,110,169],"improve":[82],"prediction.":[84],"This":[85],"paper":[86],"proposes":[87],"modeling":[90,161],"deep":[94],"learning.":[96],"Our":[97],"idea":[98],"enhance":[101],"variable":[103],"representation":[104],"by":[105],"discovering":[106],"Markov":[108],"state":[109,126],"data":[113],"a":[116,124],"K-state":[117],"long":[118],"short-term":[119],"memory":[120],"(LSTM)":[121],"model.":[122],"Such":[123],"machine":[127],"capable":[129],"complicated":[133],"semantics":[135],"highly":[137],"structured":[138],"heterogeneous":[140],"data.":[142],"Gumbel-softmax":[143],"introduced":[145],"implement":[147],"procedure":[150],"with":[151,173],"discrete":[152],"states.":[153],"Experimental":[154],"results":[155],"visual":[157],"text":[159],"illustrate":[162],"merit":[164],"proposed":[167],"prediction":[172],"limited":[174],"amount":[175],"parameters.":[177]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}