{"id":"https://openalex.org/W2219727625","doi":"https://doi.org/10.1109/allerton.2015.7447163","title":"A deep learning approach to structured signal recovery","display_name":"A deep learning approach to structured signal recovery","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2219727625","doi":"https://doi.org/10.1109/allerton.2015.7447163","mag":"2219727625"},"language":"en","primary_location":{"id":"doi:10.1109/allerton.2015.7447163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton.2015.7447163","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1508.04065","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ali Mousavi","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Mousavi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ankit B. Patel","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankit B. Patel","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"last","author":{"id":null,"display_name":"Richard G. Baraniuk","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard G. Baraniuk","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, Houston, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":17.4221,"has_fulltext":false,"cited_by_count":348,"citation_normalized_percentile":{"value":0.99671213,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1336","last_page":"1343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994000196456909,"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/autoencoder","display_name":"Autoencoder","score":0.7781999707221985},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6514000296592712},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6036999821662903},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6035000085830688},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5684000253677368},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5349000096321106},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.507099986076355},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5042999982833862},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4740999937057495}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7781999707221985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7566999793052673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7027999758720398},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6514000296592712},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6036999821662903},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6035000085830688},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5349000096321106},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.507099986076355},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C2989281035","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Signal recovery","level":3,"score":0.4496999979019165},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4230000078678131},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4212000072002411},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.384799987077713},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.375900000333786},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34049999713897705},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.251800000667572},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/allerton.2015.7447163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton.2015.7447163","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1508.04065","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1508.04065","pdf_url":"https://arxiv.org/pdf/1508.04065","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1508.04065","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1508.04065","pdf_url":"https://arxiv.org/pdf/1508.04065","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W2013035813","https://openalex.org/W2015418199","https://openalex.org/W2037642501","https://openalex.org/W2060038468","https://openalex.org/W2082029531","https://openalex.org/W2087326866","https://openalex.org/W2100495367","https://openalex.org/W2100556411","https://openalex.org/W2104266187","https://openalex.org/W2116064496","https://openalex.org/W2116581300","https://openalex.org/W2121835854","https://openalex.org/W2129638195","https://openalex.org/W2134929491","https://openalex.org/W2145096794","https://openalex.org/W2153518451","https://openalex.org/W2154232923","https://openalex.org/W2154815154","https://openalex.org/W2160547390","https://openalex.org/W2289917018","https://openalex.org/W2963322354","https://openalex.org/W4250955649","https://openalex.org/W6629815555","https://openalex.org/W6631943919","https://openalex.org/W6676481782","https://openalex.org/W6677651945","https://openalex.org/W6680955900","https://openalex.org/W6683825394"],"related_works":[],"abstract_inverted_index":{"In":[0,14,61],"this":[1],"paper,":[2],"we":[3,32,63],"develop":[4],"a":[5,34,48,58,65],"new":[6],"framework":[7,37],"for":[8],"sensing":[9,18],"and":[10,27,42,54,89],"recovering":[11],"structured":[12,49],"signals.":[13],"contrast":[15],"to":[16,78,96],"compressive":[17],"(CS)":[19],"systems":[20],"that":[21,38,46,55],"employ":[22],"linear":[23,41],"measurements,":[24,45],"sparse":[25],"representations,":[26],"computationally":[28],"complex":[29],"convex/greedy":[30],"algorithms,":[31],"introduce":[33],"deep":[35],"learning":[36],"supports":[39],"both":[40],"mildly":[43],"nonlinear":[44],"learns":[47],"representation":[50],"from":[51],"training":[52],"data,":[53],"efficiently":[56],"computes":[57],"signal":[59,91],"estimate.":[60],"particular,":[62],"apply":[64],"stacked":[66],"denoising":[67],"autoencoder":[68],"(SDA),":[69],"as":[70,94],"an":[71],"unsupervised":[72],"feature":[73],"learner.":[74],"SDA":[75],"enables":[76],"us":[77],"capture":[79],"statistical":[80],"dependencies":[81],"between":[82],"the":[83,97],"different":[84],"elements":[85],"of":[86],"certain":[87],"signals":[88],"improve":[90],"recovery":[92],"performance":[93],"compared":[95],"CS":[98],"approach.":[99]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":32},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":40},{"year":2021,"cited_by_count":52},{"year":2020,"cited_by_count":57},{"year":2019,"cited_by_count":41},{"year":2018,"cited_by_count":30},{"year":2017,"cited_by_count":14},{"year":2016,"cited_by_count":8}],"updated_date":"2026-06-20T22:02:38.213706","created_date":"2016-06-24T00:00:00"}
