{"id":"https://openalex.org/W2013278460","doi":"https://doi.org/10.1109/ijcnn.2013.6707022","title":"Noise benefits in backpropagation and deep bidirectional pre-training","display_name":"Noise benefits in backpropagation and deep bidirectional pre-training","publication_year":2013,"publication_date":"2013-08-01","ids":{"openalex":"https://openalex.org/W2013278460","doi":"https://doi.org/10.1109/ijcnn.2013.6707022","mag":"2013278460"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2013.6707022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6707022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","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/A5015927589","display_name":"Kartik Audhkhasi","orcid":"https://orcid.org/0000-0002-2340-1144"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kartik Audhkhasi","raw_affiliation_strings":["Electrical Engineering Department, University of Southern California, Los Angeles, CA","Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of Southern California, Los Angeles, CA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA#TAB#","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047871627","display_name":"Osonde Osoba","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Osonde Osoba","raw_affiliation_strings":["Electrical Engineering Department, University of Southern California, Los Angeles, CA","Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of Southern California, Los Angeles, CA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA#TAB#","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065383312","display_name":"Bart Kosko","orcid":"https://orcid.org/0000-0003-4745-8986"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bart Kosko","raw_affiliation_strings":["Electrical Engineering Department, University of Southern California, Los Angeles, CA","Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, University of Southern California, Los Angeles, CA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA#TAB#","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015927589"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":4.809,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.94769045,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9937999844551086,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9933000206947327,"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/backpropagation","display_name":"Backpropagation","score":0.8757364749908447},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7248124480247498},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5947096347808838},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5599023103713989},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.5025560855865479},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.494747132062912},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.448817640542984},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4475114643573761},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.42995917797088623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3624476194381714},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3276466727256775},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3274376690387726},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.1595999002456665},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.1352314054965973}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.8757364749908447},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7248124480247498},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5947096347808838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5599023103713989},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.5025560855865479},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.494747132062912},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.448817640542984},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4475114643573761},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.42995917797088623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3624476194381714},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3276466727256775},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3274376690387726},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.1595999002456665},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.1352314054965973},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2013.6707022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6707022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1501162218","https://openalex.org/W1601795611","https://openalex.org/W1813659000","https://openalex.org/W1965555277","https://openalex.org/W2000021165","https://openalex.org/W2014760270","https://openalex.org/W2049500688","https://openalex.org/W2049633694","https://openalex.org/W2057853719","https://openalex.org/W2057922125","https://openalex.org/W2068993558","https://openalex.org/W2075667050","https://openalex.org/W2095425517","https://openalex.org/W2099647341","https://openalex.org/W2100495367","https://openalex.org/W2104293985","https://openalex.org/W2111406701","https://openalex.org/W2111926818","https://openalex.org/W2117853077","https://openalex.org/W2124136621","https://openalex.org/W2124776405","https://openalex.org/W2125838338","https://openalex.org/W2133900104","https://openalex.org/W2136922672","https://openalex.org/W2139873797","https://openalex.org/W2141655993","https://openalex.org/W2142895703","https://openalex.org/W2156892329","https://openalex.org/W2160287763","https://openalex.org/W2160815625","https://openalex.org/W2184045248","https://openalex.org/W2210387432","https://openalex.org/W2488678869","https://openalex.org/W3037706425","https://openalex.org/W3100430487","https://openalex.org/W4211019631","https://openalex.org/W4285719527","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2116036791","https://openalex.org/W2588855097","https://openalex.org/W2092661960","https://openalex.org/W2811019791","https://openalex.org/W2949211747","https://openalex.org/W2955414824","https://openalex.org/W4226451553","https://openalex.org/W2135434361","https://openalex.org/W3178345791","https://openalex.org/W1986743941"],"abstract_inverted_index":{"We":[0,179],"prove":[1],"that":[2,22,115],"noise":[3,48,59,67,94,113,122,138,154,161,182],"can":[4,128],"speed":[5,130],"convergence":[6,131],"in":[7,175],"the":[8,23,31,45,63,72,77,84,88,93,98,102,126,133,144,159,164,172,176,181],"backpropagation":[9,24,56,85],"algorithm.":[10,166,178],"The":[11,18,41,58,90,136],"proof":[12],"consists":[13],"of":[14,30,76,87,92,101,132,163],"two":[15],"separate":[16],"results.":[17],"first":[19],"result":[20,43],"proves":[21],"algorithm":[25,35],"is":[26,140],"a":[27,52,111,118,141,147],"special":[28],"case":[29],"generalized":[32],"Expectation-Maximization":[33],"(EM)":[34],"for":[36,55],"iterative":[37],"maximum":[38],"likelihood":[39],"estimation.":[40],"second":[42],"uses":[44],"recent":[46],"EM":[47,165,177],"benefit":[49,68,95,162],"to":[50,62,71,83],"derive":[51],"sufficient":[53],"condition":[54],"training.":[57],"adds":[60],"directly":[61],"training":[64,86],"data.":[65],"A":[66],"also":[69],"applies":[70],"deep":[73],"bidirectional":[74],"pre-training":[75],"neural":[78,134],"network":[79],"as":[80,82],"well":[81],"network.":[89,135],"geometry":[91],"depends":[96],"on":[97,123],"probability":[99],"structure":[100],"neurons":[103,109,145],"at":[104],"each":[105],"layer.":[106],"Logistic":[107],"sigmoidal":[108],"produce":[110],"forbidden":[112,137],"region":[114,139],"lies":[116],"below":[117],"hyperplane.":[119],"Then":[120],"all":[121,156],"or":[124,150],"above":[125],"hyperplane":[127],"only":[129],"sphere":[142],"if":[143],"have":[146],"Gaussian":[148],"signal":[149],"activation":[151],"function.":[152],"These":[153],"benefits":[155,183],"follow":[157],"from":[158],"general":[160],"Monte":[167],"Carlo":[168],"sample":[169],"means":[170],"estimate":[171],"population":[173],"expectations":[174],"demonstrate":[180],"using":[184],"MNIST":[185],"digit":[186],"classification.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}