{"id":"https://openalex.org/W1789045941","doi":null,"title":"Learning Deep Autoencoders without Layer-wise Training.","display_name":"Learning Deep Autoencoders without Layer-wise Training.","publication_year":2014,"publication_date":"2014-05-06","ids":{"openalex":"https://openalex.org/W1789045941","mag":"1789045941"},"language":"en","primary_location":{"id":"mag:1789045941","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1405.1380v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1405.1380v2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040821130","display_name":"Yingbo Zhou","orcid":"https://orcid.org/0000-0001-6034-9667","observed_orcids":["https://orcid.org/0000-0001-6034-9667"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yingbo Zhou","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6034-9667","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5040821130"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9413999915122986,"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":0.9413999915122986,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.012500000186264515,"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/T10057","display_name":"Face and Expression Recognition","score":0.007799999788403511,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-autoencoder","display_name":"deep autoencoder","score":0.9330000281333923},{"id":"https://openalex.org/keywords/end-to-end-learning","display_name":"end-to-end learning","score":0.8989999890327454},{"id":"https://openalex.org/keywords/reconstruction-loss","display_name":"reconstruction loss","score":0.7429999709129333},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.5989999771118164},{"id":"https://openalex.org/keywords/unsupervised-classification","display_name":"unsupervised classification","score":0.7689999938011169}],"concepts":[{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.7775928974151611},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.7686358690261841},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7446521520614624},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.72690749168396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6869016885757446},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.555491030216217},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5430130958557129},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5115931630134583},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49666982889175415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46265703439712524},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42202863097190857},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41744518280029297},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11133390665054321},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06297609210014343},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:1789045941","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1405.1380v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"pmh:oai:CiteSeerX.psu:10.1.1.748.3349","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.748.3349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1405.1380.pdf","raw_type":"text"}],"best_oa_location":{"id":"mag:1789045941","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1405.1380v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W104184427","https://openalex.org/W196761320","https://openalex.org/W2100495367","https://openalex.org/W2102409316","https://openalex.org/W2136922672"],"related_works":["https://openalex.org/W1617630444","https://openalex.org/W2269675680","https://openalex.org/W1767302903","https://openalex.org/W2997183031","https://openalex.org/W193255932","https://openalex.org/W2147062276","https://openalex.org/W2025768430","https://openalex.org/W2540556213","https://openalex.org/W2145094598","https://openalex.org/W2321627895"],"abstract_inverted_index":{"Although":[0],"greedy":[1,114],"layer-wise":[2,66,115],"pre-training":[3],"has":[4],"achieved":[5],"a":[6,40],"lot":[7],"of":[8,18,23],"success":[9],"in":[10,97,106],"deep":[11],"neural":[12],"networks,":[13],"due":[14],"to":[15,48,112],"the":[16,19,24,34,65,74,79,98,107,113],"locality":[17],"method,":[20],"higher":[21],"layers":[22],"network":[25],"may":[26],"not":[27,62],"learn":[28],"representations":[29],"that":[30,46,86],"are":[31],"useful":[32],"for":[33],"original":[35],"input.":[36],"In":[37],"this":[38],"work,":[39],"novel":[41],"unsupervised":[42],"joint":[43],"training":[44,53,67],"method":[45],"tries":[47],"integrate":[49],"multiple":[50],"single":[51],"layer":[52],"objectives":[54],"into":[55],"one":[56],"global":[57],"objective":[58],"is":[59],"proposed.":[60],"It":[61],"only":[63],"mimics":[64],"scheme":[68],"locally,":[69],"but":[70],"also":[71,102],"adjusts":[72],"all":[73],"weights":[75],"together":[76],"based":[77],"on":[78],"reconstruction":[80],"loss":[81],"from":[82],"end-to-end.":[83],"Results":[84],"show":[85],"it":[87],"extracts":[88],"more":[89],"representative":[90],"features":[91],"and":[92,101],"achieves":[93,103],"better":[94],"classification":[95],"results":[96],"un-supervised":[99],"setting,":[100],"comparable":[104],"performance":[105],"supervised":[108],"setting":[109],"as":[110],"compared":[111],"approach":[116],"while":[117],"being":[118],"faster.":[119],"1":[120]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2016-06-24T00:00:00"}
