{"id":"https://openalex.org/W1971014294","doi":"https://doi.org/10.1145/2001269.2001295","title":"Unsupervised learning of hierarchical representations with convolutional deep belief networks","display_name":"Unsupervised learning of hierarchical representations with convolutional deep belief networks","publication_year":2011,"publication_date":"2011-09-23","ids":{"openalex":"https://openalex.org/W1971014294","doi":"https://doi.org/10.1145/2001269.2001295","mag":"1971014294"},"language":"en","primary_location":{"id":"doi:10.1145/2001269.2001295","doi":"https://doi.org/10.1145/2001269.2001295","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2001269.2001295","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/2001269.2001295","source":{"id":"https://openalex.org/S103482838","display_name":"Communications of the ACM","issn_l":"0001-0782","issn":["0001-0782","1557-7317"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","fnege-3","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications of the ACM","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/2001269.2001295","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108652283","display_name":"Honglak Lee","orcid":"https://orcid.org/0000-0002-4109-327X","observed_orcids":["https://orcid.org/0000-0002-4109-327X"]},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Honglak Lee","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067036768","display_name":"Roger Grosse","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405258931","display_name":"MIT Computer Science and Artificial Intelligence Laboratory","ror":"https://ror.org/01e85r662","country_code":"US","type":"facility","lineage":["https://openalex.org/I4405258931","https://openalex.org/I63966007"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roger Grosse","raw_affiliation_strings":["CSAIL, Massachusetts Institute of Technology, Cambridge, MA","CSAIL, Massachusetts Inst. of Technology, Cambridge, MA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CSAIL, Massachusetts Institute of Technology, Cambridge, MA","institution_ids":["https://openalex.org/I4405258931","https://openalex.org/I63966007"]},{"raw_affiliation_string":"CSAIL, Massachusetts Inst. of Technology, Cambridge, MA#TAB#","institution_ids":["https://openalex.org/I4405258931","https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022202456","display_name":"Rajesh Ranganath","orcid":"https://orcid.org/0000-0003-2854-3376","observed_orcids":["https://orcid.org/0000-0003-2854-3376"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajesh Ranganath","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102617494","display_name":"Andrew Y. Ng","orcid":"https://orcid.org/0000-0001-5547-3196","observed_orcids":["https://orcid.org/0000-0001-5547-3196"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Y. Ng","raw_affiliation_strings":["Stanford University, Stanford, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":17.8439,"has_fulltext":true,"cited_by_count":396,"citation_normalized_percentile":{"value":0.99409461,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"54","issue":"10","first_page":"95","last_page":"103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5565999746322632,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5565999746322632,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.14390000700950623,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.1316000074148178,"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/convolutional-deep-belief-network","display_name":"convolutional deep belief network","score":0.9950000047683716},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.8799999952316284},{"id":"https://openalex.org/keywords/hierarchical-generative-models","display_name":"hierarchical generative models","score":0.9620000123977661},{"id":"https://openalex.org/keywords/translation-invariance","display_name":"translation invariance","score":0.9150000214576721},{"id":"https://openalex.org/keywords/top-down-inference","display_name":"top-down inference","score":0.8460000157356262},{"id":"https://openalex.org/keywords/image-recognition","display_name":"image recognition","score":0.47699999809265137},{"id":"https://openalex.org/keywords/natural-scene-recognition","display_name":"natural scene recognition","score":0.4740000069141388}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8011992573738098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7679634094238281},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.740534245967865},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.710390567779541},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6445804238319397},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.628986656665802},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5286795496940613},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.502945601940155},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5018689632415771},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45641475915908813},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4471196234226227},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3975731432437897},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.38912254571914673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2001269.2001295","doi":"https://doi.org/10.1145/2001269.2001295","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2001269.2001295","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/2001269.2001295","source":{"id":"https://openalex.org/S103482838","display_name":"Communications of the ACM","issn_l":"0001-0782","issn":["0001-0782","1557-7317"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","fnege-3","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications of the ACM","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/2001269.2001295","doi":"https://doi.org/10.1145/2001269.2001295","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2001269.2001295","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/2001269.2001295","source":{"id":"https://openalex.org/S103482838","display_name":"Communications of the ACM","issn_l":"0001-0782","issn":["0001-0782","1557-7317"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","fnege-3","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications of the ACM","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G440206934","display_name":null,"funder_award_id":"FA8750-05-2-0249","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1971014294.pdf","grobid_xml":"https://content.openalex.org/works/W1971014294.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W66118917","https://openalex.org/W114565888","https://openalex.org/W189596042","https://openalex.org/W1553861040","https://openalex.org/W1798871656","https://openalex.org/W1994197834","https://openalex.org/W2043285245","https://openalex.org/W2099866409","https://openalex.org/W2100495367","https://openalex.org/W2101933716","https://openalex.org/W2102116870","https://openalex.org/W2105464770","https://openalex.org/W2108138754","https://openalex.org/W2108665656","https://openalex.org/W2110361616","https://openalex.org/W2110798204","https://openalex.org/W2116064496","https://openalex.org/W2116825644","https://openalex.org/W2120432001","https://openalex.org/W2122922389","https://openalex.org/W2124372976","https://openalex.org/W2125663122","https://openalex.org/W2133257461","https://openalex.org/W2136922672","https://openalex.org/W2137234026","https://openalex.org/W2139427956","https://openalex.org/W2142615865","https://openalex.org/W2145889472","https://openalex.org/W2147800946","https://openalex.org/W2155904486","https://openalex.org/W2158164339","https://openalex.org/W2159291644","https://openalex.org/W2162915993","https://openalex.org/W2165720259","https://openalex.org/W2165828254","https://openalex.org/W2168002178","https://openalex.org/W2597289420","https://openalex.org/W2963909185","https://openalex.org/W2990138404"],"related_works":["https://openalex.org/W2130325614","https://openalex.org/W7099409833","https://openalex.org/W1972596620","https://openalex.org/W2173520492","https://openalex.org/W2083227311","https://openalex.org/W2184481212","https://openalex.org/W1963079441","https://openalex.org/W2134905716","https://openalex.org/W2293682279","https://openalex.org/W3084884320"],"abstract_inverted_index":{"There":[0],"has":[1],"been":[2],"much":[3],"interest":[4],"in":[5,82],"unsupervised":[6],"learning":[7],"of":[8,79,105],"hierarchical":[9,43,126],"generative":[10,44],"models":[11,21],"such":[12,20,98],"as":[13,99],"deep":[14,38],"belief":[15,39],"networks":[16],"(DBNs);":[17],"however,":[18],"scaling":[19],"to":[22,48,65],"full-sized,":[23],"high-dimensional":[24],"images":[25,104],"remains":[26],"a":[27,42,72,83],"difficult":[28],"problem.":[29],"To":[30],"address":[31],"this":[32],"problem,":[33],"we":[34],"present":[35],"the":[36,77,91],"convolutional":[37],"network":[40],",":[41,71],"model":[45,53,123],"that":[46,75,90,121],"scales":[47],"realistic":[49],"image":[50],"sizes.":[51],"This":[52],"is":[54,68],"translation-invariant":[55],"and":[56,60,107,119,128],"supports":[57],"efficient":[58],"bottom-up":[59],"top-down":[61],"probabilistic":[62,69],"inference.":[63],"Key":[64],"our":[66,122],"approach":[67],"max-pooling":[70],"novel":[73],"technique":[74],"shrinks":[76],"representations":[78],"higher":[80],"layers":[81],"probabilistically":[84],"sound":[85],"way.":[86],"Our":[87],"experiments":[88],"show":[89,120],"algorithm":[92],"learns":[93],"useful":[94],"high-level":[95],"visual":[96,116],"features,":[97],"object":[100],"parts,":[101],"from":[102],"unlabeled":[103],"objects":[106],"natural":[108],"scenes.":[109],"We":[110],"demonstrate":[111],"excellent":[112],"performance":[113],"on":[114],"several":[115],"recognition":[117],"tasks":[118],"can":[124],"perform":[125],"(bottom-up":[127],"top-down)":[129],"inference":[130],"over":[131],"full-sized":[132],"images.":[133]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":40},{"year":2019,"cited_by_count":34},{"year":2018,"cited_by_count":45},{"year":2017,"cited_by_count":32},{"year":2016,"cited_by_count":49},{"year":2015,"cited_by_count":38},{"year":2014,"cited_by_count":24},{"year":2013,"cited_by_count":20},{"year":2012,"cited_by_count":13}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}
