{"id":"https://openalex.org/W3161998735","doi":"https://doi.org/10.1109/icassp39728.2021.9415073","title":"Graph Signal Denoising Via Unrolling Networks","display_name":"Graph Signal Denoising Via Unrolling Networks","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161998735","doi":"https://doi.org/10.1109/icassp39728.2021.9415073","mag":"3161998735"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9415073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9415073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5066373402","display_name":"Siheng Chen","orcid":"https://orcid.org/0000-0002-9144-0583"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Siheng Chen","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005913897","display_name":"Yonina C. Eldar","orcid":"https://orcid.org/0000-0003-4358-5304"},"institutions":[{"id":"https://openalex.org/I53964585","display_name":"Weizmann Institute of Science","ror":"https://ror.org/0316ej306","country_code":"IL","type":"education","lineage":["https://openalex.org/I53964585"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Yonina C. Eldar","raw_affiliation_strings":["Weizmann Institute of Science"],"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science","institution_ids":["https://openalex.org/I53964585"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066373402"],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":0.4079,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67426184,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5290","last_page":"5294"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9451000094413757,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6823875308036804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6576309204101562},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5930954217910767},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.5142561793327332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4545847177505493},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4275754988193512},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42079412937164307},{"id":"https://openalex.org/keywords/graph-bandwidth","display_name":"Graph bandwidth","score":0.4200688600540161},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39097779989242554},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.317193865776062},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.27487021684646606},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.10028982162475586}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6823875308036804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6576309204101562},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5930954217910767},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.5142561793327332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4545847177505493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4275754988193512},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42079412937164307},{"id":"https://openalex.org/C134727501","wikidata":"https://www.wikidata.org/wiki/Q5597073","display_name":"Graph bandwidth","level":5,"score":0.4200688600540161},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39097779989242554},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.317193865776062},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.27487021684646606},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.10028982162475586}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9415073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9415073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1978333359","https://openalex.org/W1991252559","https://openalex.org/W2004559848","https://openalex.org/W2016926874","https://openalex.org/W2101491865","https://openalex.org/W2117569556","https://openalex.org/W2118103795","https://openalex.org/W2158787690","https://openalex.org/W2247102326","https://openalex.org/W2408728757","https://openalex.org/W2558748708","https://openalex.org/W2586906523","https://openalex.org/W2796431263","https://openalex.org/W2938935291","https://openalex.org/W2963670588","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2972675027","https://openalex.org/W2998785217","https://openalex.org/W3015741275","https://openalex.org/W3035545045","https://openalex.org/W3133902371","https://openalex.org/W4287990713","https://openalex.org/W4297733535","https://openalex.org/W6677645113","https://openalex.org/W6684515591","https://openalex.org/W6726873649","https://openalex.org/W6771584377"],"related_works":["https://openalex.org/W2387796150","https://openalex.org/W4293577752","https://openalex.org/W1990319978","https://openalex.org/W2043490270","https://openalex.org/W2386722878","https://openalex.org/W2188097034","https://openalex.org/W4225155218","https://openalex.org/W233850645","https://openalex.org/W4385546444","https://openalex.org/W2525531780"],"abstract_inverted_index":{"We":[0,41,67],"propose":[1],"an":[2,30,43],"interpretable":[3],"graph":[4,14,18,26,65,70,80,104,143,151,171,175],"neural":[5,94,144],"network":[6,54],"framework":[7],"to":[8,24,62,84],"denoise":[9],"single":[10,53,149],"or":[11],"multiple":[12],"noisy":[13,79,103],"signals.":[15,66],"The":[16],"proposed":[17,115,160],"unrolling":[19,23,71],"networks":[20,72,161],"expand":[21],"algorithm":[22,46],"the":[25,33,57,69,77,86,90,114,153,159],"domain":[27],"and":[28,125,128,141,165,174],"provide":[29],"interpretation":[31],"of":[32,93,107,158,170],"architecture":[34],"design":[35],"from":[36,101],"a":[37,52,148],"signal":[38,110],"processing":[39],"perspective.":[40],"unroll":[42],"iterative":[44],"denoising":[45,64,135,139,147,173],"by":[47],"mapping":[48],"each":[49],"iteration":[50],"into":[51],"layer":[55],"where":[56,76],"feed-forward":[58],"process":[59],"is":[60,162],"equivalent":[61],"iteratively":[63],"train":[68],"through":[73],"unsupervised":[74],"learning,":[75],"input":[78,102],"signals":[81],"are":[82],"used":[83],"supervise":[85],"networks.":[87,145],"By":[88],"leveraging":[89],"learning":[91],"ability":[92],"networks,":[95],"we":[96,117],"adaptively":[97],"capture":[98],"appropriate":[99],"priors":[100],"signals,":[105],"instead":[106],"manually":[108],"choosing":[109],"priors.":[111],"To":[112],"validate":[113],"methods,":[116],"conduct":[118],"extensive":[119],"experiments":[120],"on":[121],"both":[122],"real-world":[123],"datasets":[124],"simulated":[126],"datasets,":[127],"demonstrate":[129],"that":[130,169],"our":[131],"methods":[132],"have":[133],"smaller":[134],"errors":[136],"than":[137,168],"conventional":[138],"algorithms":[140],"state-of-the-art":[142],"For":[146],"smooth":[150],"signal,":[152],"normalized":[154],"mean":[155],"square":[156],"error":[157],"around":[163],"40%":[164],"60%":[166],"lower":[167],"Laplacian":[172],"wavelets,":[176],"respectively.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
