{"id":"https://openalex.org/W3161535217","doi":"https://doi.org/10.1109/icassp39728.2021.9414588","title":"Kalman Optimizer for Consistent Gradient Descent","display_name":"Kalman Optimizer for Consistent Gradient Descent","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161535217","doi":"https://doi.org/10.1109/icassp39728.2021.9414588","mag":"3161535217"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414588","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":"conference-paper","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/A5080003280","display_name":"Xingyi Yang","orcid":"https://orcid.org/0000-0002-1603-9829"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xingyi Yang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, La Jolla, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080003280"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9987999796867371,"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/T10320","display_name":"Neural Networks and Applications","score":0.9987999796867371,"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/T10057","display_name":"Face and Expression Recognition","score":0.9896000027656555,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9833999872207642,"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/kalman-filter","display_name":"Kalman filter","score":0.7704271674156189},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.7015523314476013},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.6963107585906982},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6828612685203552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6548065543174744},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.5188692808151245},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.5187520980834961},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4532642066478729},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4369214177131653},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.42683714628219604},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39069628715515137},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3319125175476074},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2234528660774231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22011512517929077}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.7704271674156189},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.7015523314476013},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.6963107585906982},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6828612685203552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6548065543174744},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.5188692808151245},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.5187520980834961},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4532642066478729},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4369214177131653},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.42683714628219604},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39069628715515137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3319125175476074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2234528660774231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22011512517929077},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414588","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":20,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1522301498","https://openalex.org/W1980287119","https://openalex.org/W2105934661","https://openalex.org/W2145832734","https://openalex.org/W2146502635","https://openalex.org/W2194775991","https://openalex.org/W2619251062","https://openalex.org/W2898416980","https://openalex.org/W2903382683","https://openalex.org/W2951732061","https://openalex.org/W2964121744","https://openalex.org/W6600284362","https://openalex.org/W6631190155","https://openalex.org/W6681435938","https://openalex.org/W6681468881","https://openalex.org/W6687483927","https://openalex.org/W6738768603","https://openalex.org/W6755386810","https://openalex.org/W6757107679"],"related_works":["https://openalex.org/W4206024512","https://openalex.org/W2053335193","https://openalex.org/W2394115825","https://openalex.org/W4246354917","https://openalex.org/W2188632562","https://openalex.org/W2533888311","https://openalex.org/W1996382496","https://openalex.org/W2895097035","https://openalex.org/W4206903459","https://openalex.org/W2754816816"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNN)":[3],"are":[4],"typically":[5],"optimized":[6],"using":[7,18],"stochastic":[8,19,45,72],"gradient":[9,17,30,59,73,88],"descent":[10,74],"(SGD).":[11],"However,":[12],"the":[13,16,57,77,81,94,100,103],"estimation":[14,55,69],"of":[15,56,80,96,102],"samples":[20],"tends":[21],"to":[22,52,85,115],"be":[23],"noisy":[24,87],"and":[25,32,118],"unreliable,":[26],"resulting":[27],"in":[28,71],"large":[29],"variance":[31,70],"bad":[33],"convergence.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"propose":[39],"Kalman":[40,50,105],"Optimizor":[41],"(KO),":[42],"an":[43,62],"efficient":[44],"optimization":[46,109],"algorithm":[47],"that":[48],"adopts":[49],"filter":[51],"make":[53],"consistent":[54],"local":[58],"by":[60,75],"solving":[61],"adaptive":[63],"filtering":[64],"problem.":[65],"Our":[66],"method":[67],"reduces":[68],"incorporating":[76],"historic":[78],"state":[79],"optimization.":[82],"It":[83],"aims":[84],"improve":[86],"direction":[89],"as":[90,92],"well":[91],"accelerate":[93],"convergence":[95],"learning.":[97],"We":[98],"demonstrate":[99],"effectiveness":[101],"proposed":[104],"Optimizer":[106],"under":[107],"various":[108],"tasks":[110],"where":[111],"it":[112],"is":[113,123],"shown":[114],"achieve":[116],"superior":[117],"robust":[119],"performance.":[120],"The":[121],"code":[122],"available":[124],"at":[125],"https://github.com/Adamdad/Filter-Gradient-Decent.":[126]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
