{"id":"https://openalex.org/W2553683256","doi":"https://doi.org/10.1109/mlsp.2016.7738828","title":"Sparse-coded net model and applications","display_name":"Sparse-coded net model and applications","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2553683256","doi":"https://doi.org/10.1109/mlsp.2016.7738828","mag":"2553683256"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2016.7738828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5012323997","display_name":"Youngjune Gwon","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Youngjune Gwon","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029707299","display_name":"Miriam Cha","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miriam Cha","raw_affiliation_strings":["Harvard University","MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]},{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061064027","display_name":"William M. Campbell","orcid":"https://orcid.org/0000-0003-1657-5872"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William Campbell","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066867900","display_name":"H. T. Kung","orcid":"https://orcid.org/0000-0002-3348-3788"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"H. T. Kung","raw_affiliation_strings":["Harvard University"],"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026993630","display_name":"Charlie K. Dagli","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charlie K. Dagli","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5012323997"],"corresponding_institution_ids":["https://openalex.org/I4210122954"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07806643,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T10812","display_name":"Human Pose and Action Recognition","score":0.9984999895095825,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7818304300308228},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.707307755947113},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7019369602203369},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.648238480091095},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5401327610015869},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5355866551399231},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5344758629798889},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5086544156074524},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.47484686970710754},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.46590977907180786},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.45056572556495667},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4330289661884308},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.42784279584884644},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4198518991470337},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4184541404247284},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07954907417297363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7818304300308228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.707307755947113},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7019369602203369},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.648238480091095},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5401327610015869},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5355866551399231},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5344758629798889},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5086544156074524},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.47484686970710754},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.46590977907180786},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.45056572556495667},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4330289661884308},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.42784279584884644},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4198518991470337},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4184541404247284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07954907417297363},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp.2016.7738828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:dash.harvard.edu:1/37374078","is_oa":false,"landing_page_url":"https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374078","pdf_url":null,"source":{"id":"https://openalex.org/S4306401540","display_name":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I136199984","host_organization_name":"Harvard University","host_organization_lineage":["https://openalex.org/I136199984"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1985133440","https://openalex.org/W2005876975","https://openalex.org/W2012914973","https://openalex.org/W2055423828","https://openalex.org/W2063978378","https://openalex.org/W2086384421","https://openalex.org/W2105464873","https://openalex.org/W2106277773","https://openalex.org/W2115549875","https://openalex.org/W2118858186","https://openalex.org/W2128659236","https://openalex.org/W2135046866","https://openalex.org/W2136922672","https://openalex.org/W2145094598","https://openalex.org/W2153635508","https://openalex.org/W2160547390","https://openalex.org/W2213844264","https://openalex.org/W2402967400","https://openalex.org/W2517097820","https://openalex.org/W2997574889","https://openalex.org/W3118608800","https://openalex.org/W6677446445","https://openalex.org/W6677919164","https://openalex.org/W6681096077","https://openalex.org/W6688555639","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2890544631","https://openalex.org/W2067062989","https://openalex.org/W2998105788","https://openalex.org/W4205656132","https://openalex.org/W2111634407","https://openalex.org/W3004790527","https://openalex.org/W2203155458","https://openalex.org/W2783282829","https://openalex.org/W2138494306","https://openalex.org/W2539392819"],"abstract_inverted_index":{"As":[0],"an":[1,12],"unsupervised":[2],"learning":[3,19,64,158],"method,":[4],"sparse":[5,24,39,55,86],"coding":[6,25,56,87],"can":[7,117,130],"discover":[8],"high-level":[9],"representations":[10,40],"for":[11,31],"input":[13],"in":[14,96,111,144,174],"a":[15,32,62,81,100,112,153],"large":[16],"variety":[17],"of":[18,46],"problems.":[20],"Under":[21],"semi-supervised":[22,104,157],"settings,":[23],"is":[26],"used":[27],"to":[28,123,133,176],"extract":[29],"features":[30],"supervised":[33,48,69,134],"task":[34,49,70],"such":[35],"as":[36,61,159,161],"classification.":[37],"While":[38],"learned":[41],"from":[42],"unlabeled":[43],"data":[44],"independently":[45],"the":[47,68,125],"perform":[50],"well,":[51],"we":[52,77,106],"argue":[53],"that":[54,84,116],"should":[57],"also":[58],"be":[59,131],"built":[60],"holistic":[63],"unit":[65],"optimizing":[66],"on":[67],"objectives":[71],"more":[72],"explicitly.":[73],"In":[74],"this":[75],"paper,":[76],"propose":[78],"sparse-coded":[79,101,128,139],"net,":[80],"feedforward":[82],"model":[83],"integrates":[85],"and":[88,92,147,171],"task-driven":[89],"output":[90],"layers,":[91],"describe":[93],"training":[94],"methods":[95],"detail.":[97],"After":[98],"pretraining":[99],"net":[102,129,140],"via":[103],"learning,":[105],"optimize":[107],"its":[108],"task-specific":[109],"performance":[110,164],"novel":[113],"backpropagation":[114],"algorithm":[115],"traverse":[118],"nonlinear":[119],"feature":[120],"pooling":[121],"operators":[122],"update":[124],"dictionary.":[126],"Thus,":[127],"applied":[132],"dictionary":[135],"learning.":[136],"We":[137],"evaluate":[138],"with":[141],"classification":[142,163],"problems":[143],"sound,":[145],"image,":[146],"text":[148],"data.":[149],"The":[150],"results":[151],"confirm":[152],"significant":[154],"improvement":[155],"over":[156],"well":[160],"superior":[162],"against":[165],"deep":[166],"stacked":[167],"autoencoder":[168],"neural":[169],"network":[170],"GMM-SVM":[172],"pipelines":[173],"small":[175],"medium-scale":[177],"settings.":[178]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}