{"id":"https://openalex.org/W2139772141","doi":null,"title":"Recurrent Neural Networks for Missing or Asynchronous Data","display_name":"Recurrent Neural Networks for Missing or Asynchronous Data","publication_year":1995,"publication_date":"1995-11-27","ids":{"openalex":"https://openalex.org/W2139772141","mag":"2139772141"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.48.4989","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.4989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dcs.shef.ac.uk/~ljupco/papers/miss-nips8.ps.gz","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5086198262","display_name":"Yoshua Bengio","orcid":"https://orcid.org/0000-0002-9322-3515","observed_orcids":["https://orcid.org/0000-0002-9322-3515"]},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yoshua Bengio","raw_affiliation_strings":["Dept. Informatique et, Recherche Op\u00e9rationnelle, Universit\u00e9 de Montr\u00e9al, Montreal, Qc#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-9322-3515","affiliations":[{"raw_affiliation_string":"Dept. Informatique et, Recherche Op\u00e9rationnelle, Universit\u00e9 de Montr\u00e9al, Montreal, Qc#TAB#","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070037854","display_name":"Fran\u00e7ois Gingras","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fran\u00e7ois Gingras","raw_affiliation_strings":["Dept. Informatique et, Recherche Op\u00e9rationnelle, Universit\u00e9 de Montr\u00e9al, Montreal, Qc#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Informatique et, Recherche Op\u00e9rationnelle, Universit\u00e9 de Montr\u00e9al, Montreal, Qc#TAB#","institution_ids":["https://openalex.org/I70931966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70931966"],"apc_list":null,"apc_paid":null,"fwci":1.3061,"has_fulltext":false,"cited_by_count":85,"citation_normalized_percentile":{"value":0.82950942,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"395","last_page":"401"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9951000213623047,"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/T10320","display_name":"Neural Networks and Applications","score":0.9951000213623047,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.996999979019165},{"id":"https://openalex.org/keywords/missing-data","display_name":"missing data","score":0.9309999942779541},{"id":"https://openalex.org/keywords/asynchronous-data","display_name":"asynchronous data","score":0.9350000023841858},{"id":"https://openalex.org/keywords/sequential-data","display_name":"sequential data","score":0.8169999718666077},{"id":"https://openalex.org/keywords/learning-from-examples","display_name":"learning from examples","score":0.49000000953674316}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.9385894536972046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6321429014205933},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6061410307884216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5196689367294312},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4876922369003296},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.483134001493454},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4738864302635193},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.4432342052459717},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.43785637617111206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4263342022895813},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.41550207138061523},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39367157220840454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35096168518066406},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2563984990119934},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2412937879562378},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.1670295000076294},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.48.4989","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.4989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dcs.shef.ac.uk/~ljupco/papers/miss-nips8.ps.gz","raw_type":"text"},{"id":"mag:2139772141","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/1126-recurrent-neural-networks-for-missing-or-asynchronous-data.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1528915954","https://openalex.org/W1881179843","https://openalex.org/W2048060899","https://openalex.org/W2100206501","https://openalex.org/W2115046692","https://openalex.org/W2128221272","https://openalex.org/W2137440383","https://openalex.org/W2144847675","https://openalex.org/W2895369475"],"related_works":["https://openalex.org/W7101094710","https://openalex.org/W4407572318","https://openalex.org/W3005542031","https://openalex.org/W7108210690","https://openalex.org/W4292433406","https://openalex.org/W1496712898","https://openalex.org/W6989495890","https://openalex.org/W4306317258","https://openalex.org/W4410774044","https://openalex.org/W4398226330"],"abstract_inverted_index":{"In":[0],"this":[1,26],"paper":[2],"we":[3],"propose":[4],"recurrent":[5],"neural":[6],"networks":[7],"with":[8],"feedback":[9],"into":[10],"the":[11,23,37,43,57,70,78,81,88,91,95,108,112,149],"input":[12,38,58],"units":[13],"for":[14,31,51,111,156],"handling":[15],"two":[16],"types":[17],"of":[18,36,56,72,77,90,115,137,143],"data":[19,33],"analysis":[20],"problems.":[21],"On":[22,42],"one":[24],"hand,":[25,45],"scheme":[27],"can":[28,47],"be":[29,49],"used":[30,50],"static":[32],"when":[34,54],"some":[35,55],"variables":[39,59,93,110,136],"are":[40,60,63],"missing.":[41],"other":[44],"it":[46,99],"also":[48],"sequential":[52],"data,":[53],"missing":[61,79,92,109],"or":[62],"available":[64],"at":[65],"different":[66],"frequencies.":[67],"Unlike":[68],"in":[69,107],"case":[71],"probabilistic":[73],"models":[74],"(e.g.":[75],"Gaussian)":[76],"variables,":[80],"network":[82],"does":[83],"not":[84],"attempt":[85],"to":[86,121,146],"model":[87],"distribution":[89,151],"given":[94],"observed":[96],"variables.":[97,154],"Instead":[98],"is":[100],"a":[101,117],"more":[102],"\"discriminant\"":[103],"approach":[104],"that":[105],"fills":[106],"sole":[113],"purpose":[114],"minimizing":[116],"learning":[118,144],"criterion":[119],"(e.g.,":[120],"minimize":[122],"an":[123],"output":[124],"error).":[125],"1":[126],"Introduction":[127],"Learning":[128],"from":[129],"examples":[130],"implies":[131],"discovering":[132],"certain":[133],"relations":[134],"between":[135,152],"interest.":[138],"The":[139],"most":[140],"general":[141],"form":[142],"requires":[145],"essentially":[147],"capture":[148],"joint":[150],"these":[153],"However,":[155],"many":[157],"spe...":[158]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
