{"id":"https://openalex.org/W2281948289","doi":"https://doi.org/10.5555/2789272.2912118","title":"Marginalizing stacked linear denoising autoencoders","display_name":"Marginalizing stacked linear denoising autoencoders","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2281948289","doi":"https://doi.org/10.5555/2789272.2912118","mag":"2281948289"},"language":"en","primary_location":{"id":"mag:2281948289","is_oa":false,"landing_page_url":"http://jmlr.csail.mit.edu/papers/volume16/chen15c/chen15c.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null},"type":"article","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/A5100699702","display_name":"Minmin Chen","orcid":"https://orcid.org/0000-0002-7342-9022"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minmin Chen","raw_affiliation_strings":["Criteo, Palo Alto, CA#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-7342-9022","affiliations":[{"raw_affiliation_string":"Criteo, Palo Alto, CA#TAB#","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006380227","display_name":"Kilian Q. Weinberger","orcid":null},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kilian Q. Weinberger","raw_affiliation_strings":["Dept. of Comput. Sci. & Eng., Washington Univ. in St. Louis, St. Louis, MO"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Comput. Sci. & Eng., Washington Univ. in St. Louis, St. Louis, MO","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100820808","display_name":"Zhixiang Xu","orcid":"https://orcid.org/0000-0003-4713-5124"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhixiang Xu","raw_affiliation_strings":["Dept. of Comput. Sci. & Eng., Washington Univ. in St. Louis, St. Louis, MO"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Comput. Sci. & Eng., Washington Univ. in St. Louis, St. Louis, MO","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101544732","display_name":"Fei Sha","orcid":"https://orcid.org/0000-0002-9382-0010"},"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":"Fei Sha","raw_affiliation_strings":["Computer Science Department,University of Southern California, Los Angeles, CA"],"raw_orcid":"https://orcid.org/0000-0002-9382-0010","affiliations":[{"raw_affiliation_string":"Computer Science Department,University of Southern California, Los Angeles, CA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.1115,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.96459768,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"16","issue":"1","first_page":"3849","last_page":"3875"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994000196456909,"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/T11309","display_name":"Music and Audio Processing","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/benchmark","display_name":"Benchmark (surveying)","score":0.7797616720199585},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6895420551300049},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6735855340957642},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.662650465965271},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6258991360664368},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5781797766685486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5250955820083618},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.48815661668777466},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47990214824676514},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4310106337070465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4136781692504883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34757864475250244},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2297385036945343},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.17800447344779968},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14959010481834412},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09324795007705688}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7797616720199585},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6895420551300049},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6735855340957642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.662650465965271},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6258991360664368},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5781797766685486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5250955820083618},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.48815661668777466},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47990214824676514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4310106337070465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4136781692504883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34757864475250244},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2297385036945343},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.17800447344779968},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14959010481834412},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09324795007705688},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2281948289","is_oa":false,"landing_page_url":"http://jmlr.csail.mit.edu/papers/volume16/chen15c/chen15c.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W35527955","https://openalex.org/W1498436455","https://openalex.org/W1506806321","https://openalex.org/W1544444001","https://openalex.org/W1663973292","https://openalex.org/W1665214252","https://openalex.org/W1677409904","https://openalex.org/W1722318740","https://openalex.org/W1904365287","https://openalex.org/W1978394996","https://openalex.org/W2025768430","https://openalex.org/W2059993991","https://openalex.org/W2068463433","https://openalex.org/W2070996757","https://openalex.org/W2078626246","https://openalex.org/W2090923791","https://openalex.org/W2095252775","https://openalex.org/W2096873754","https://openalex.org/W2104094955","https://openalex.org/W2104867159","https://openalex.org/W2105523772","https://openalex.org/W2107789863","https://openalex.org/W2112483442","https://openalex.org/W2118045473","https://openalex.org/W2118585731","https://openalex.org/W2119821739","https://openalex.org/W2120354757","https://openalex.org/W2121857350","https://openalex.org/W2127099514","https://openalex.org/W2131953535","https://openalex.org/W2138382875","https://openalex.org/W2140262144","https://openalex.org/W2142742813","https://openalex.org/W2144211451","https://openalex.org/W2145094598","https://openalex.org/W2147152072","https://openalex.org/W2149466042","https://openalex.org/W2153635508","https://openalex.org/W2158108973","https://openalex.org/W2158542502","https://openalex.org/W2163302275","https://openalex.org/W2169495281","https://openalex.org/W2215421138","https://openalex.org/W2218318129","https://openalex.org/W2811380766","https://openalex.org/W2949821452"],"related_works":["https://openalex.org/W2025768430","https://openalex.org/W2165698076","https://openalex.org/W2158542502","https://openalex.org/W2145094598","https://openalex.org/W2194775991","https://openalex.org/W2115403315","https://openalex.org/W2159526726","https://openalex.org/W2903257334","https://openalex.org/W3125619802","https://openalex.org/W2507341862","https://openalex.org/W1853900790","https://openalex.org/W2945669993","https://openalex.org/W2098753191","https://openalex.org/W2894115892","https://openalex.org/W2183227662","https://openalex.org/W1588343734","https://openalex.org/W3047534117","https://openalex.org/W3047700752","https://openalex.org/W2903204032","https://openalex.org/W3154083231"],"abstract_inverted_index":{"Stacked":[0,55],"denoising":[1],"autoencoders":[2],"(SDAs)":[3],"have":[4,16],"been":[5],"successfully":[6],"used":[7],"to":[8,74,79,99,110],"learn":[9,32,100],"new":[10],"representations":[11,35,139],"for":[12],"domain":[13],"adaptation.":[14],"They":[15],"attained":[17],"record":[18],"accuracy":[19],"on":[20],"standard":[21],"benchmark":[22,155],"tasks":[23],"of":[24,65,72,83,124,130],"sentiment":[25],"analysis":[26],"across":[27],"different":[28],"text":[29],"domains.":[30],"SDAs":[31],"robust":[33],"data":[34,42],"by":[36,141],"reconstruction,":[37],"recovering":[38],"original":[39],"features":[40],"from":[41],"that":[43,60],"are":[44,143],"artificially":[45],"corrupted":[46],"with":[47],"noise.":[48],"In":[49,77],"this":[50],"paper,":[51],"we":[52],"propose":[53],"marginalized":[54],"Linear":[56],"Denoising":[57],"Autoencoder":[58],"(mSLDA)":[59],"addresses":[61],"two":[62,128],"crucial":[63],"limitations":[64],"SDAs:":[66],"high":[67],"computational":[68],"cost":[69],"and":[70,87],"lack":[71],"scalability":[73],"high-dimensional":[75],"features.":[76],"contrast":[78],"SDAs,":[80,149],"our":[81],"approach":[82],"mSLDA":[84,142],"marginalizes":[85],"noise":[86],"thus":[88],"does":[89],"not":[90],"require":[91],"stochastic":[92],"gradient":[93],"descent":[94],"or":[95],"other":[96],"optimization":[97],"algorithms":[98],"parameters":[101],"--":[102],"in":[103,120,154],"fact,":[104],"the":[105,138,147],"linear":[106],"formulation":[107],"gives":[108],"rise":[109],"a":[111,134],"closed-form":[112],"solution.":[113],"Consequently,":[114],"mSLDA,":[115],"which":[116],"can":[117],"be":[118],"implemented":[119],"only":[121],"20":[122],"lines":[123],"MATLAB\u2122,":[125],"is":[126],"about":[127],"orders":[129],"magnitude":[131],"faster":[132],"than":[133],"corresponding":[135],"SDA.":[136],"Furthermore,":[137],"learnt":[140],"as":[144,146],"effective":[145],"traditional":[148],"attaining":[150],"almost":[151],"identical":[152],"accuracies":[153],"tasks.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2025-10-10T17:16:08.811792","created_date":"2025-10-10T00:00:00"}
