{"id":"https://openalex.org/W4402351481","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650994","title":"Multi-Objective Optimization for Sparse Deep Multi-Task Learning","display_name":"Multi-Objective Optimization for Sparse Deep Multi-Task Learning","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351481","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650994"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5000500403","display_name":"S\u00e8djro Salomon Hotegni","orcid":"https://orcid.org/0000-0002-5682-467X"},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sedjro S. Hotegni","raw_affiliation_strings":["Paderborn University,Department of Computer Science,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Paderborn University,Department of Computer Science,Germany","institution_ids":["https://openalex.org/I206945453"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001134145","display_name":"Manuel Berkemeier","orcid":"https://orcid.org/0000-0002-3958-2277"},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Manuel Berkemeier","raw_affiliation_strings":["Paderborn University,Department of Computer Science,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Paderborn University,Department of Computer Science,Germany","institution_ids":["https://openalex.org/I206945453"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049416946","display_name":"Sebastian Peitz","orcid":"https://orcid.org/0000-0002-3389-793X"},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian Peitz","raw_affiliation_strings":["Paderborn University,Department of Computer Science,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Paderborn University,Department of Computer Science,Germany","institution_ids":["https://openalex.org/I206945453"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I206945453"],"apc_list":null,"apc_paid":null,"fwci":4.77,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.96097006,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9983999729156494,"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/T12676","display_name":"Machine Learning and ELM","score":0.9907000064849854,"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":0.7541201114654541},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6283345818519592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5502925515174866},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4714024066925049},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.43267756700515747},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4177761673927307},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08850923180580139},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.08340510725975037}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7541201114654541},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6283345818519592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5502925515174866},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4714024066925049},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.43267756700515747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4177761673927307},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08850923180580139},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.08340510725975037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W122140505","https://openalex.org/W398727469","https://openalex.org/W641771830","https://openalex.org/W1499692130","https://openalex.org/W1553373771","https://openalex.org/W1968535060","https://openalex.org/W1997188340","https://openalex.org/W2057624533","https://openalex.org/W2060846151","https://openalex.org/W2076767658","https://openalex.org/W2086743688","https://openalex.org/W2100717205","https://openalex.org/W2117502083","https://openalex.org/W2143381319","https://openalex.org/W2143419558","https://openalex.org/W2157570139","https://openalex.org/W2164878629","https://openalex.org/W2251324968","https://openalex.org/W2520760693","https://openalex.org/W2564125978","https://openalex.org/W2725202000","https://openalex.org/W2788842424","https://openalex.org/W2800805097","https://openalex.org/W2935694433","https://openalex.org/W2950673314","https://openalex.org/W2963430933","https://openalex.org/W2963703618","https://openalex.org/W2963854351","https://openalex.org/W3085046840","https://openalex.org/W3111675103","https://openalex.org/W3118608800","https://openalex.org/W3121410165","https://openalex.org/W3128096387","https://openalex.org/W3140662957","https://openalex.org/W3141797743","https://openalex.org/W3181074959","https://openalex.org/W3191428434","https://openalex.org/W4225322509","https://openalex.org/W4225916762","https://openalex.org/W4235644068","https://openalex.org/W4283157527","https://openalex.org/W4287117188","https://openalex.org/W4287363917","https://openalex.org/W4292864963","https://openalex.org/W4293417218","https://openalex.org/W4302008876","https://openalex.org/W4385804999","https://openalex.org/W6720323505","https://openalex.org/W6732814185","https://openalex.org/W6743446608","https://openalex.org/W6748323323","https://openalex.org/W6751281758","https://openalex.org/W6754005058","https://openalex.org/W6787972765","https://openalex.org/W6790503700"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Different":[0],"conflicting":[1],"optimization":[2,122],"criteria":[3],"arise":[4],"naturally":[5],"in":[6,18,50],"various":[7],"Deep":[8,71,155],"Learning":[9,182],"scenarios.":[10],"These":[11],"can":[12,87],"address":[13,139],"different":[14],"main":[15,26],"tasks":[16,29],"(i.e.,":[17],"the":[19,44,51,85,93,118,140,186,191,209],"setting":[20],"of":[21,43,92,103,120,147,167,188],"Multi-Task":[22,156],"Learning),":[23],"but":[24],"also":[25,143],"and":[27,127,142],"secondary":[28],"such":[30,124],"as":[31,125],"loss":[32],"minimization":[33],"versus":[34],"sparsity.":[35],"The":[36,106],"usual":[37],"approach":[38],"is":[39,213],"a":[40,59,64,101,151,163],"simple":[41],"weighting":[42],"criteria,":[45],"which":[46,158],"formally":[47],"only":[48],"works":[49],"convex":[52],"setting.":[53],"In":[54],"this":[55,82],"paper,":[56],"we":[57,184,201],"present":[58],"Multi-Objective":[60],"Optimization":[61],"algorithm":[62,86],"using":[63,112],"modified":[65],"Weighted":[66],"Chebyshev":[67],"scalarization":[68,83],"for":[69],"training":[70,194],"Neural":[72],"Networks":[73],"(DNNs)":[74],"with":[75,150,162],"respect":[76],"to":[77,100,138,169,204,208],"several":[78],"tasks.":[79,175],"By":[80],"employing":[81],"technique,":[84],"identify":[88],"all":[89],"optimal":[90],"solutions":[91],"original":[94],"problem":[95],"while":[96,131],"reducing":[97],"its":[98,198],"complexity":[99],"sequence":[102],"single-objective":[104],"problems.":[105],"simplified":[107],"problems":[108],"are":[109,159,202],"then":[110],"solved":[111],"an":[113],"Augmented":[114],"Lagrangian":[115],"method,":[116],"enabling":[117],"use":[119],"popular":[121],"techniques":[123],"Adam":[126],"Stochastic":[128],"Gradient":[129],"Descent,":[130],"efficaciously":[132],"handling":[133],"constraints.":[134],"Our":[135],"work":[136],"aims":[137],"(economical":[141],"ecological)":[144],"sustainability":[145],"issue":[146],"DNN":[148],"models,":[149,157],"particular":[152],"focus":[153],"on":[154,173,179],"typically":[160],"designed":[161],"very":[164],"large":[165],"number":[166],"weights":[168],"perform":[170],"equally":[171],"well":[172],"multiple":[174],"Through":[176],"experiments":[177],"conducted":[178],"two":[180],"Machine":[181],"datasets,":[183],"demonstrate":[185],"possibility":[187],"adaptively":[189],"sparsifying":[190],"model":[192],"during":[193],"without":[195],"significantly":[196],"impacting":[197],"performance,":[199],"if":[200],"willing":[203],"apply":[205],"task-specific":[206],"adaptations":[207],"network":[210],"weights.":[211],"Code":[212],"available":[214],"at":[215],"https://github.com/salomonhotegni/MDMTN.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}