{"id":"https://openalex.org/W3016167088","doi":"https://doi.org/10.1109/icassp40776.2020.9053799","title":"Upscaling Vector Approximate Message Passing","display_name":"Upscaling Vector Approximate Message Passing","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3016167088","doi":"https://doi.org/10.1109/icassp40776.2020.9053799","mag":"3016167088"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053799","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/20.500.11820/ffe46cad-804b-465d-afbf-f7643a85b56d","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019675655","display_name":"Nikolajs Skuratovs","orcid":"https://orcid.org/0000-0002-0760-8583"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Nikolajs Skuratovs","raw_affiliation_strings":["School of Engineering and Electronics, The University of Edinburgh"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Electronics, The University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113674176","display_name":"Michael Davies","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Michael Davies","raw_affiliation_strings":["School of Engineering and Electronics, The University of Edinburgh"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Electronics, The University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019675655"],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":0.8803,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.6810522,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4757","last_page":"4761"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9987999796867371,"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/message-passing","display_name":"Message passing","score":0.6509369611740112},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6498870253562927},{"id":"https://openalex.org/keywords/conjugate-gradient-method","display_name":"Conjugate gradient method","score":0.5929741263389587},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5710458755493164},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5579238533973694},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4800363779067993},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.43986976146698},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4339648485183716},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.38386210799217224},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.254582941532135},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.19967320561408997},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08662068843841553}],"concepts":[{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.6509369611740112},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6498870253562927},{"id":"https://openalex.org/C81184566","wikidata":"https://www.wikidata.org/wiki/Q1191895","display_name":"Conjugate gradient method","level":2,"score":0.5929741263389587},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5710458755493164},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5579238533973694},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4800363779067993},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.43986976146698},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4339648485183716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38386210799217224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.254582941532135},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.19967320561408997},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08662068843841553},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053799","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/ffe46cad-804b-465d-afbf-f7643a85b56d","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/portal/en/publications/upscaling-vector-approximate-message-passing(ffe46cad-804b-465d-afbf-f7643a85b56d).html","pdf_url":"http://hdl.handle.net/20.500.11820/ffe46cad-804b-465d-afbf-f7643a85b56d","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":""},{"id":"pmh:oai:pure.ed.ac.uk:openaire/ffe46cad-804b-465d-afbf-f7643a85b56d","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/ffe46cad-804b-465d-afbf-f7643a85b56d","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Skuratovs, N & Davies, M 2020, Upscaling Vector Approximate Message Passing. in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . vol. 2020, 19787434 , International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Institute of Electrical and Electronics Engineers, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, 4/05/20. https://doi.org/10.1109/ICASSP40776.2020.9053799","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:publications/ffe46cad-804b-465d-afbf-f7643a85b56d","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/portal/en/publications/upscaling-vector-approximate-message-passing(ffe46cad-804b-465d-afbf-f7643a85b56d).html","pdf_url":"http://hdl.handle.net/20.500.11820/ffe46cad-804b-465d-afbf-f7643a85b56d","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":""},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3016167088.pdf","grobid_xml":"https://content.openalex.org/works/W3016167088.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1822073640","https://openalex.org/W2011697680","https://openalex.org/W2054640142","https://openalex.org/W2056370875","https://openalex.org/W2145568341","https://openalex.org/W2552441117","https://openalex.org/W2778503646","https://openalex.org/W2810458093","https://openalex.org/W2945463326","https://openalex.org/W2963602028","https://openalex.org/W2963676935","https://openalex.org/W2964047892","https://openalex.org/W3102442912","https://openalex.org/W4297795527","https://openalex.org/W6638507750","https://openalex.org/W6653304808","https://openalex.org/W6729952562","https://openalex.org/W6752664415","https://openalex.org/W6763010633"],"related_works":["https://openalex.org/W2978729728","https://openalex.org/W2544771389","https://openalex.org/W2034060070","https://openalex.org/W4229957265","https://openalex.org/W2386899346","https://openalex.org/W3082608044","https://openalex.org/W2131505227","https://openalex.org/W2375597358","https://openalex.org/W3087397739","https://openalex.org/W4288966080"],"abstract_inverted_index":{"In":[0,102],"this":[1,103],"paper":[2],"we":[3,105],"consider":[4],"the":[5,29,72,123,126],"problem":[6],"of":[7,12,25,69,100,125],"recovering":[8],"a":[9,75,107],"signal":[10],"x":[11],"size":[13,26],"N":[14],"from":[15],"noisy":[16],"and":[17,120],"compressed":[18],"measurements":[19],"y":[20],"=":[21],"Ax":[22],"+":[23],"w":[24],"M,":[27],"where":[28],"measurement":[30],"matrix":[31],"A":[32,51],"is":[33,60,85,116],"right-orthogonally":[34],"invariant":[35],"(ROI).":[36],"Vector":[37],"Approximate":[38],"Message":[39],"Passing":[40],"(VAMP)":[41],"demonstrates":[42],"great":[43],"reconstruction":[44],"results":[45],"for":[46,91],"even":[47],"highly":[48],"ill-conditioned":[49],"matrices":[50],"in":[52],"relatively":[53],"few":[54],"iterations.":[55],"However,":[56],"performing":[57],"each":[58],"iteration":[59],"challenging":[61],"due":[62],"to":[63,87,96,118,122],"either":[64],"computational":[65],"or":[66],"memory":[67],"point":[68],"view.":[70],"On":[71],"other":[73],"hand,":[74],"recently":[76],"proposed":[77],"Conjugate":[78],"Gradient":[79],"(CG)":[80],"Expectation":[81],"Propagation":[82],"(CG-EP)":[83],"framework":[84],"able":[86],"sacrifice":[88],"some":[89],"performance":[90],"efficiency,":[92],"but":[93],"requires":[94],"access":[95],"exact":[97],"singular":[98],"spectrum":[99],"A.":[101],"work":[104],"develop":[106],"CG-VAMP":[108],"algorithm":[109],"that":[110],"does":[111],"not":[112],"require":[113],"such":[114],"information,":[115],"feasible":[117],"implement":[119],"converges":[121],"neighborhood":[124],"original":[127],"VAMP.":[128]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
