{"id":"https://openalex.org/W3196591432","doi":"https://doi.org/10.1109/lsp.2021.3101421","title":"Learning to Balance the Learning Rates Between Various Modalities via Adaptive Tracking Factor","display_name":"Learning to Balance the Learning Rates Between Various Modalities via Adaptive Tracking Factor","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3196591432","doi":"https://doi.org/10.1109/lsp.2021.3101421","mag":"3196591432"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3101421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3101421","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5100376520","display_name":"Sun Ya","orcid":"https://orcid.org/0000-0002-8765-9945"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ya Sun","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010270301","display_name":"Sijie Mai","orcid":"https://orcid.org/0000-0001-9763-375X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijie Mai","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9763-375X","affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056953478","display_name":"Haifeng Hu","orcid":"https://orcid.org/0000-0002-4884-323X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Hu","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4884-323X","affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100376520"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":5.0372,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.96039708,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"28","issue":null,"first_page":"1650","last_page":"1654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9952999949455261,"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/T12676","display_name":"Machine Learning and ELM","score":0.9952999949455261,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9915000200271606,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.991100013256073,"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/computer-science","display_name":"Computer science","score":0.7720310688018799},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7390821576118469},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.7357510328292847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6531351208686829},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5661698579788208},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.5406960248947144},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5088610649108887},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5079174637794495},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.4722917675971985},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4344120919704437},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.325227290391922},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27880680561065674}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7720310688018799},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7390821576118469},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.7357510328292847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6531351208686829},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5661698579788208},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.5406960248947144},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5088610649108887},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5079174637794495},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.4722917675971985},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4344120919704437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.325227290391922},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27880680561065674},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2021.3101421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3101421","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G8307738215","display_name":null,"funder_award_id":"62076262","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1522301498","https://openalex.org/W1679826675","https://openalex.org/W1972412716","https://openalex.org/W1994616650","https://openalex.org/W2029996593","https://openalex.org/W2095176743","https://openalex.org/W2250539671","https://openalex.org/W2556418146","https://openalex.org/W2560730294","https://openalex.org/W2742093937","https://openalex.org/W2785523195","https://openalex.org/W2787581402","https://openalex.org/W2883409523","https://openalex.org/W2886193235","https://openalex.org/W2910191085","https://openalex.org/W2962718314","https://openalex.org/W2962767366","https://openalex.org/W2962843521","https://openalex.org/W2963693848","https://openalex.org/W2963702064","https://openalex.org/W2964010806","https://openalex.org/W2964051877","https://openalex.org/W2964121744","https://openalex.org/W2964213933","https://openalex.org/W2964216663","https://openalex.org/W2964346351","https://openalex.org/W3035333188","https://openalex.org/W3041730883","https://openalex.org/W3048195943","https://openalex.org/W3105484484","https://openalex.org/W3105983073","https://openalex.org/W3108764796","https://openalex.org/W3127521573","https://openalex.org/W3136644942","https://openalex.org/W3164173240","https://openalex.org/W4294558607","https://openalex.org/W6600284362","https://openalex.org/W6631190155","https://openalex.org/W6738964360","https://openalex.org/W6742288159","https://openalex.org/W6747620207","https://openalex.org/W6748551036","https://openalex.org/W6753277404","https://openalex.org/W6784051049","https://openalex.org/W6786051229","https://openalex.org/W6786244873"],"related_works":["https://openalex.org/W73545470","https://openalex.org/W4224266612","https://openalex.org/W2383394264","https://openalex.org/W4320153225","https://openalex.org/W4293261942","https://openalex.org/W3125968744","https://openalex.org/W203959209","https://openalex.org/W2110287964","https://openalex.org/W2167701463","https://openalex.org/W4307407935"],"abstract_inverted_index":{"Multimodal":[0],"networks":[1,65,92],"with":[2,49],"richer":[3],"information":[4],"contents":[5],"should":[6],"always":[7,22],"outperform":[8],"the":[9,23,50,76,85,96,104,115,124,150],"unimodal":[10,54,89,154],"counterparts.":[11],"In":[12,71],"our":[13,169],"experiment,":[14],"however,":[15],"we":[16,83],"observe":[17],"that":[18,103,168],"this":[19,72],"is":[20,120],"not":[21],"case.":[24],"Prior":[25],"efforts":[26],"on":[27,63,80,130,163],"multimodal":[28,47,64,81,91,164],"tasks":[29],"mainly":[30],"tend":[31],"to":[32,74,122,146,157],"design":[33],"a":[34,45,131],"uniform":[35],"optimization":[36,106,142],"algorithm":[37,107],"for":[38,99,108,127],"all":[39],"modalities,":[40,101],"and":[41,90,139,148],"yet":[42],"only":[43],"obtain":[44],"sub-optimal":[46,153],"representation":[48],"fusion":[51],"of":[52,88],"under-optimized":[53],"representations,":[55],"which":[56],"are":[57,144],"still":[58],"challenged":[59],"by":[60,67,93],"performance":[61,79],"drop":[62],"caused":[66],"heterogeneity":[68],"among":[69],"modalities.":[70],"work,":[73],"remove":[75],"slowdowns":[77],"in":[78],"tasks,":[82],"decouple":[84],"learning":[86,97,125],"procedures":[87],"dynamically":[94],"balancing":[95],"rates":[98],"various":[100],"so":[102],"modality-specific":[105],"each":[109,128],"modality":[110,129],"can":[111],"be":[112],"obtained.":[113],"Specifically,":[114],"adaptive":[116,135],"tracking":[117],"factor":[118],"(ATF)":[119],"introduced":[121],"adjust":[123],"rate":[126],"real-time":[132],"basis.":[133],"Furthermore,":[134],"convergent":[136],"equalization":[137],"(ACE)":[138],"bilevel":[140],"directional":[141],"(BDO)":[143],"proposed":[145],"equalize":[147],"update":[149],"ATF,":[151],"avoiding":[152],"representations":[155],"due":[156],"overfitting":[158],"or":[159],"underfitting.":[160],"Extensive":[161],"experiments":[162],"sentiment":[165],"analysis":[166],"demonstrate":[167],"method":[170],"achieves":[171],"superior":[172],"performance.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2026-05-28T09:10:13.091523","created_date":"2025-10-10T00:00:00"}