{"id":"https://openalex.org/W2607813869","doi":"https://doi.org/10.1109/icpr.2016.7900029","title":"Deep Sparse-coded Network (DSN)","display_name":"Deep Sparse-coded Network (DSN)","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2607813869","doi":"https://doi.org/10.1109/icpr.2016.7900029","mag":"2607813869"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7900029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7900029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://nrs.harvard.edu/urn-3:HUL.InstRepos:34903188","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012323997","display_name":"Youngjune Gwon","orcid":null},"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":"Youngjune Gwon","raw_affiliation_strings":["Harvard University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029707299","display_name":"Miriam Cha","orcid":null},"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":"Miriam Cha","raw_affiliation_strings":["Harvard University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"last","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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2801851002"],"apc_list":null,"apc_paid":null,"fwci":2.6633,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.916112,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"2610","last_page":"2615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991999864578247,"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/T10860","display_name":"Speech and Audio Processing","score":0.9970999956130981,"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/autoencoder","display_name":"Autoencoder","score":0.7303402423858643},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.6695874333381653},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6394416689872742},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6066297292709351},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5762748718261719},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5649240016937256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44081151485443115},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4336956739425659},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43238401412963867},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.42708563804626465},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4250425398349762},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41353198885917664},{"id":"https://openalex.org/keywords/hessian-matrix","display_name":"Hessian matrix","score":0.41193655133247375},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34742555022239685},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22185537219047546}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7303402423858643},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.6695874333381653},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6394416689872742},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6066297292709351},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5762748718261719},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5649240016937256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44081151485443115},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4336956739425659},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43238401412963867},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.42708563804626465},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4250425398349762},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41353198885917664},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.41193655133247375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34742555022239685},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22185537219047546},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr.2016.7900029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7900029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:dash.harvard.edu:1/34903188","is_oa":true,"landing_page_url":"http://nrs.harvard.edu/urn-3:HUL.InstRepos:34903188","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:dash.harvard.edu:1/34903188","is_oa":true,"landing_page_url":"http://nrs.harvard.edu/urn-3:HUL.InstRepos:34903188","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2063978378","https://openalex.org/W2099866409","https://openalex.org/W2100218328","https://openalex.org/W2100495367","https://openalex.org/W2105464873","https://openalex.org/W2112796928","https://openalex.org/W2118858186","https://openalex.org/W2128659236","https://openalex.org/W2135046866","https://openalex.org/W2136922672","https://openalex.org/W2145094598","https://openalex.org/W2160547390","https://openalex.org/W2163202312","https://openalex.org/W2997574889","https://openalex.org/W6677919164","https://openalex.org/W6680156768","https://openalex.org/W6681096077","https://openalex.org/W6683694789"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2953234277","https://openalex.org/W2611031068","https://openalex.org/W4297051394","https://openalex.org/W2398880078","https://openalex.org/W2799042817"],"abstract_inverted_index":{"We":[0,72,116,138],"present":[1],"Deep":[2],"Sparse-coded":[3],"Network":[4],"(DSN),":[5],"a":[6,21,31,47,52,57,118,156],"deep":[7],"architecture":[8],"based":[9],"on":[10,61,161],"multilayer":[11],"sparse":[12,27,42,53,58,84,96,133],"coding.":[13],"It":[14],"has":[15],"been":[16],"considered":[17],"difficult":[18],"to":[19,67,88,104,122],"learn":[20],"useful":[22],"feature":[23],"hierarchy":[24],"by":[25,76],"stacking":[26],"coding":[28,43,59,97,134],"layers":[29],"in":[30,46],"straightforward":[32],"manner.":[33],"The":[34],"primary":[35],"reason":[36],"is":[37],"the":[38,62,69,94,114,124,128,145,149],"modeling":[39,70],"assumption":[40],"for":[41,93],"that":[44],"takes":[45],"dense":[48,90],"input":[49,91],"and":[50,135,148,151],"yields":[51],"output":[54,63],"vector.":[55],"Applying":[56],"layer":[60],"of":[64],"another":[65],"tends":[66],"violate":[68],"assumption.":[71],"overcome":[73],"this":[74],"shortcoming":[75],"interlacing":[77],"nonlinear":[78,101],"pooling":[79],"units.":[80],"Average-":[81],"or":[82],"max-pooled":[83],"codes":[85],"are":[86],"aggregated":[87],"form":[89],"vectors":[92],"next":[95],"layer.":[98],"Pooling":[99],"achieves":[100],"activation":[102],"analogous":[103],"neural":[105],"networks":[106],"while":[107],"not":[108],"introducing":[109],"diminished":[110],"gradient":[111],"flows":[112],"during":[113],"training.":[115],"introduce":[117],"novel":[119],"backpropagation":[120],"algorithm":[121],"finetune":[123],"proposed":[125],"DSN":[126,143],"beyond":[127],"pretraining":[129],"via":[130],"greedy":[131],"layerwise":[132],"dictionary":[136],"learning.":[137],"build":[139],"an":[140],"experimental":[141],"4-layer":[142],"with":[144],"\u21131-regularized":[146],"LARS":[147],"greedy-\u21130OMP,":[150],"demonstrate":[152],"superior":[153],"performance":[154],"over":[155],"similarly-configured":[157],"stacked":[158],"autoencoder":[159],"(SAE)":[160],"CIFAR-10.":[162]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2017-05-05T00:00:00"}