{"id":"https://openalex.org/W3183065628","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534263","title":"Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression","display_name":"Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3183065628","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534263","mag":"3183065628"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534263","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534263","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2107.01711","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038525019","display_name":"Grzegorz Dudek","orcid":"https://orcid.org/0000-0002-2285-0327"},"institutions":[{"id":"https://openalex.org/I130294970","display_name":"Cz\u0119stochowa University of Technology","ror":"https://ror.org/046awyn59","country_code":"PL","type":"education","lineage":["https://openalex.org/I130294970"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Grzegorz Dudek","raw_affiliation_strings":["Czestochowa University of Technology,Department of Electrical Engineering,Czestochowa,Poland","Czestochowa University of Technology"],"affiliations":[{"raw_affiliation_string":"Czestochowa University of Technology,Department of Electrical Engineering,Czestochowa,Poland","institution_ids":["https://openalex.org/I130294970"]},{"raw_affiliation_string":"Czestochowa University of Technology","institution_ids":["https://openalex.org/I130294970"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5038525019"],"corresponding_institution_ids":["https://openalex.org/I130294970"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0938407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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/T10320","display_name":"Neural Networks and Applications","score":0.9962000250816345,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9962000250816345,"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":0.9269238710403442},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7096560001373291},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6969367265701294},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6040700674057007},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5718305706977844},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5522779822349548},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5291412472724915},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47426140308380127},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4374043345451355},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.4276609420776367},{"id":"https://openalex.org/keywords/feed-forward","display_name":"Feed forward","score":0.4240753948688507},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14154160022735596},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13632303476333618}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9269238710403442},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7096560001373291},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6969367265701294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6040700674057007},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5718305706977844},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5522779822349548},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5291412472724915},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47426140308380127},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4374043345451355},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.4276609420776367},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.4240753948688507},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14154160022735596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13632303476333618},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534263","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534263","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2107.01711","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2107.01711","pdf_url":"https://arxiv.org/pdf/2107.01711","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3183065628","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2107.01711","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2107.01711","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2107.01711","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2107.01711","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2107.01711","pdf_url":"https://arxiv.org/pdf/2107.01711","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G412955222","display_name":null,"funder_award_id":"020/RID/2018/19","funder_id":"https://openalex.org/F4320322733","funder_display_name":"Ministerstwo Edukacji i Nauki"},{"id":"https://openalex.org/G5699012070","display_name":null,"funder_award_id":"2017/27/B/ST6/01804","funder_id":"https://openalex.org/F4320322650","funder_display_name":"Narodowym Centrum Nauki"}],"funders":[{"id":"https://openalex.org/F4320322650","display_name":"Narodowym Centrum Nauki","ror":"https://ror.org/03ha2q922"},{"id":"https://openalex.org/F4320322733","display_name":"Ministerstwo Edukacji i Nauki","ror":"https://ror.org/05dwvd537"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3183065628.pdf","grobid_xml":"https://content.openalex.org/works/W3183065628.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W177905271","https://openalex.org/W1554944419","https://openalex.org/W1786513448","https://openalex.org/W1986278072","https://openalex.org/W2063978378","https://openalex.org/W2097021318","https://openalex.org/W2100495367","https://openalex.org/W2286961399","https://openalex.org/W2560247988","https://openalex.org/W2593382986","https://openalex.org/W2753648062","https://openalex.org/W2906686987","https://openalex.org/W2912162873","https://openalex.org/W2981174158","https://openalex.org/W3089855701","https://openalex.org/W3108558997","https://openalex.org/W3141142801"],"related_works":["https://openalex.org/W3204416478","https://openalex.org/W193255932","https://openalex.org/W3106369320","https://openalex.org/W2998107848","https://openalex.org/W3080216294","https://openalex.org/W3205859423","https://openalex.org/W2931737270","https://openalex.org/W3036407970","https://openalex.org/W2901800056","https://openalex.org/W3200659000","https://openalex.org/W3198316162","https://openalex.org/W3170336723","https://openalex.org/W2773850097","https://openalex.org/W2763600295","https://openalex.org/W3211678467","https://openalex.org/W2807498578","https://openalex.org/W2209990155","https://openalex.org/W2981802957","https://openalex.org/W3035442710","https://openalex.org/W3027170666"],"abstract_inverted_index":{"Feedforward":[0],"neural":[1],"networks":[2],"are":[3],"widely":[4],"used":[5],"as":[6],"universal":[7],"predictive":[8],"models":[9],"to":[10,57,87,100,120],"fit":[11],"data":[12],"distribution.":[13],"Common":[14],"gradient-based":[15],"learning,":[16,153],"however,":[17],"suffers":[18],"from":[19],"many":[20],"drawbacks":[21],"making":[22],"the":[23,44,50,59,112,122,146,150,164],"training":[24,45],"process":[25,46],"ineffective":[26],"and":[27,142],"time-consuming.":[28],"Alternative":[29],"randomized":[30,53,135],"learning":[31,54,106,132,136],"does":[32,155],"not":[33,156],"use":[34],"gradients":[35],"but":[36],"selects":[37],"hidden":[38,125],"node":[39],"parameters":[40],"randomly.":[41],"This":[42,72],"makes":[43],"extremely":[47],"fast.":[48],"However,":[49],"problem":[51],"in":[52,160],"is":[55,166],"how":[56,99,119],"determine":[58,121],"random":[60,114],"parameters.":[61],"A":[62],"recently":[63,139],"proposed":[64,138,147],"method":[65,73,86,107,165],"uses":[66],"autoencoders":[67,109],"for":[68,140],"unsupervised":[69],"parameter":[70],"learning.":[71],"showed":[74],"superior":[75],"performance":[76],"on":[77],"classification":[78],"tasks.":[79],"In":[80],"this":[81,85],"work,":[82],"we":[83,97],"apply":[84],"regression":[88,141],"problems,":[89],"and,":[90],"finding":[91],"that":[92,110,144],"it":[93,154],"has":[94],"some":[95],"drawbacks,":[96],"show":[98],"improve":[101],"it.":[102],"We":[103,116,127],"propose":[104,118],"a":[105],"of":[108,124,149],"controls":[111],"produced":[113],"weights.":[115],"also":[117],"biases":[123],"nodes.":[126],"empirically":[128],"compare":[129],"autoencoder":[130,151],"based":[131,152],"with":[133],"other":[134],"methods":[137],"find":[143],"despite":[145],"improvement":[148],"outperform":[157],"its":[158,171],"competitors":[159],"fitting":[161],"accuracy.":[162],"Moreover,":[163],"much":[167],"more":[168],"complex":[169],"than":[170],"competitors.":[172]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2022-07-25T00:00:00"}
