{"id":"https://openalex.org/W4391583858","doi":"https://doi.org/10.1109/icdmw60847.2023.00086","title":"Context Normalization Layer with Applications","display_name":"Context Normalization Layer with Applications","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4391583858","doi":"https://doi.org/10.1109/icdmw60847.2023.00086"},"language":"en","primary_location":{"id":"doi:10.1109/icdmw60847.2023.00086","doi":"https://doi.org/10.1109/icdmw60847.2023.00086","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icdmw60847.2023.00086","pdf_url":null,"source":{"id":"https://openalex.org/S4393918563","display_name":"IEEE ... International Conference on Data Mining workshops","issn_l":"2375-9259","issn":["2375-9259"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2303.07651","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031081299","display_name":"Bilal Faye","orcid":"https://orcid.org/0000-0001-7852-7003","observed_orcids":["https://orcid.org/0000-0001-7852-7003"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Bilal Faye","raw_affiliation_strings":["Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210156583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047336564","display_name":"Hanane Azzag","orcid":"https://orcid.org/0000-0001-6876-0688","observed_orcids":["https://orcid.org/0000-0001-6876-0688"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hanane Azzag","raw_affiliation_strings":["Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210156583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023422312","display_name":"Mustapha Lebbah","orcid":"https://orcid.org/0000-0001-7245-6371","observed_orcids":["https://orcid.org/0000-0001-7245-6371"]},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4390039268","display_name":"Donn\u00e9es et algorithmes pour une ville intelligente et durable","ror":"https://ror.org/01xta2p78","country_code":"FR","type":"facility","lineage":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039268"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mustapha Lebbah","raw_affiliation_strings":["University of Versailles, Universit\u00e9 Paris-Saclay,David Lab,Versailles,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Versailles, Universit\u00e9 Paris-Saclay,David Lab,Versailles,France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I4390039268"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038326315","display_name":"Mohamed Djallel Dilmi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]},{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mohamed-Djallel Dilmi","raw_affiliation_strings":["Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France","LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210156583"]},{"raw_affiliation_string":"LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012408243","display_name":"Djamel Bouchaffra","orcid":"https://orcid.org/0000-0002-9850-907X","observed_orcids":["https://orcid.org/0000-0002-9850-907X"]},"institutions":[{"id":"https://openalex.org/I4210102186","display_name":"Centre de D\u00e9veloppement des Technologies Avanc\u00e9es","ror":"https://ror.org/01ay87255","country_code":"DZ","type":"facility","lineage":["https://openalex.org/I4210102186"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Djamel Bouchaffra","raw_affiliation_strings":["Center for Development of Advanced Technologies Algiers,Algeria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Development of Advanced Technologies Algiers,Algeria","institution_ids":["https://openalex.org/I4210102186"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":null,"first_page":"615","last_page":"624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.41110000014305115,"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":0.41110000014305115,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3732999861240387,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.10570000112056732,"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/batch-normalization","display_name":"batch normalization","score":0.8159999847412109},{"id":"https://openalex.org/keywords/gaussian-mixture-model","display_name":"Gaussian mixture model","score":0.8610000014305115},{"id":"https://openalex.org/keywords/concept-classification","display_name":"concept classification","score":0.5740000009536743},{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.4580000042915344},{"id":"https://openalex.org/keywords/internal-covariate-shift","display_name":"internal covariate shift","score":0.8429999947547913},{"id":"https://openalex.org/keywords/convergence-rate","display_name":"convergence rate","score":0.5519999861717224},{"id":"https://openalex.org/keywords/generalization","display_name":"generalization","score":0.6779999732971191},{"id":"https://openalex.org/keywords/deep-neural-network-training","display_name":"deep neural network training","score":0.5770000219345093}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.924830973148346},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.758105456829071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6866950988769531},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6529953479766846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6364914774894714},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4857974052429199},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45022106170654297},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4252844750881195},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41942501068115234},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/icdmw60847.2023.00086","doi":"https://doi.org/10.1109/icdmw60847.2023.00086","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icdmw60847.2023.00086","pdf_url":null,"source":{"id":"https://openalex.org/S4393918563","display_name":"IEEE ... International Conference on Data Mining workshops","issn_l":"2375-9259","issn":["2375-9259"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2303.07651","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.07651","pdf_url":"https://arxiv.org/pdf/2303.07651","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-04456071v1","doi":null,"is_oa":true,"landing_page_url":"https://hal.science/hal-04456071","pdf_url":"https://hal.science/hal-04456071v1/file/normalization.pdf","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2023 IEEE International Conference on Data Mining (ICDM 2023). workshop Deep Learning and Clustering (DLC), Dec 2023, Shangai, China","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:HAL:hal-04457540v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-04457540","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2023 IEEE International Conference on Data Mining Workshops (ICDMW), Dec 2023, Shanghai, France. pp.615-624, ⟨10.1109/ICDMW60847.2023.00086⟩","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.2303.07651","doi":"https://doi.org/10.48550/arxiv.2303.07651","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.07651","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2303.07651","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.07651","pdf_url":"https://arxiv.org/pdf/2303.07651","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391583858.pdf","grobid_xml":"https://content.openalex.org/works/W4391583858.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1836465849","https://openalex.org/W2068260221","https://openalex.org/W2073459066","https://openalex.org/W2116456623","https://openalex.org/W2138621090","https://openalex.org/W2188333210","https://openalex.org/W2529099292","https://openalex.org/W2545656684","https://openalex.org/W2626778328","https://openalex.org/W2775795276","https://openalex.org/W2805157522","https://openalex.org/W2908510526","https://openalex.org/W2953759675","https://openalex.org/W2963334011","https://openalex.org/W2963890275","https://openalex.org/W2964121744","https://openalex.org/W2964137095","https://openalex.org/W3091050448","https://openalex.org/W3092609815","https://openalex.org/W3094502228","https://openalex.org/W3169361860","https://openalex.org/W4238404964","https://openalex.org/W4255421341","https://openalex.org/W4287660544","https://openalex.org/W6784333009"],"related_works":["https://openalex.org/W4404402851","https://openalex.org/W6920540753","https://openalex.org/W4399592106","https://openalex.org/W4393213634","https://openalex.org/W4312674412","https://openalex.org/W2945195708","https://openalex.org/W2996418505","https://openalex.org/W4403598197","https://openalex.org/W4402353731","https://openalex.org/W4416052887"],"abstract_inverted_index":{"Deep":[0],"neural":[1,242,268],"networks":[2],"(DNNs)":[3],"have":[4],"gained":[5],"prominence":[6],"in":[7,68,299,358],"many":[8],"areas":[9],"such":[10,128],"as":[11,129,205],"computer":[12],"vision":[13],"(CV),":[14],"natural":[15],"language":[16],"processing":[17],"(NLP),":[18],"robotics,":[19],"and":[20,25,31,45,78,97,134,164,280,297,304,312,321,332,352],"bioinformatics.":[21],"While":[22],"their":[23,49],"deep":[24,355],"complex":[26],"structure":[27],"enables":[28],"powerful":[29],"representation":[30],"hierarchical":[32],"learning,":[33,74],"it":[34],"poses":[35],"serious":[36],"challenges":[37],"(e.g.,":[38],"internal":[39],"covariate":[40],"shift,":[41],"vanishing/exploding":[42],"gradients,":[43],"overfitting,":[44],"computational":[46,174],"complexity),":[47],"during":[48],"training":[50,154],"phase.":[51],"Neuron":[52],"activity":[53],"normalization":[54,83,93,138],"is":[55,263,273],"an":[56],"effective":[57],"strategy":[58],"that":[59,189,198,214,217,235,247],"lives":[60],"up":[61],"to":[62,109,160,177,223,340],"these":[63,124,182],"challenges.":[64],"This":[65],"procedure":[66],"consists":[67],"promoting":[69],"stability,":[70],"creating":[71],"a":[72,150,155,186,206,224,241,251,254,266,315,322],"balanced":[73],"improving":[75],"performance":[76,110,305,323],"generalization":[77],"gradient":[79],"flow":[80],"efficiency.":[81],"Traditional":[82],"methods":[84],"often":[85],"overlook":[86],"inherent":[87,337],"dataset":[88],"relationships.":[89],"For":[90],"example,":[91],"batch":[92],"(BN)":[94],"estimates":[95],"mean":[96],"standard":[98],"deviation":[99],"from":[100],"randomly":[101],"constructed":[102],"mini-batches":[103],"(composed":[104],"of":[105,116,208,226,233,258,293,301,319,325,335],"unrelated":[106],"samples),":[107],"leading":[108],"dependence":[111],"solely":[112],"on":[113,287],"the":[114,199,231,259,291,330,333,342],"size":[115,144],"mini-batches,":[117],"without":[118],"accounting":[119],"for":[120,237],"data":[121,200,218,238,338,350],"correlation":[122],"within":[123,250],"batches.":[125],"Conventional":[126],"techniques":[127],"Layer":[130],"Normalization,":[131,133],"Instance":[132],"Group":[135],"Normalization":[136,147],"estimate":[137],"parameters":[139,262],"per":[140],"instance,":[141],"addressing":[142],"mini-batch":[143],"issues.":[145],"Mixture":[146],"(MN)":[148],"utilizes":[149],"two-step":[151],"process:":[152],"(i)":[153],"Gaussian":[156,209,227,260,343],"mixture":[157,207],"model":[158],"(GMM)":[159],"determine":[161],"components":[162,261],"parameters,":[163],"(ii)":[165],"normalizing":[166],"activations":[167],"accordingly.":[168],"MN":[169,213,298,313],"outperforms":[170,310],"BN":[171,296,311],"but":[172],"incurs":[173],"overhead":[175],"due":[176],"GMM":[178],"usage.":[179],"To":[180],"overcome":[181],"limitations,":[183],"we":[184,190],"propose":[185],"novel":[187],"methodology":[188],"named":[191],"\"Context":[192],"Normalization\"":[193],"(CN).":[194],"Our":[195,346],"approach":[196,348],"assumes":[197,215],"distribution":[201],"can":[202],"be":[203],"represented":[204],"components.":[210],"However,":[211],"unlike":[212],"a-priori":[216],"are":[219,248,278],"partitioned":[220],"with":[221,314],"respect":[222],"set":[225],"distributions,":[228],"CN":[229,272,294,309],"introduces":[230],"notion":[232],"concept":[234,270],"accounts":[236],"relationship":[239],"via":[240],"network":[243],"classification":[244],"scheme.":[245],"Samples":[246],"gathered":[249],"cluster":[252],"define":[253],"context.":[255],"The":[256],"estimation":[257],"conducted":[264,286],"through":[265],"supervised":[267],"network-based":[269],"classification.":[271],"more":[274],"precise":[275],"when":[276],"clusters":[277],"thick":[279],"not":[281],"sparse.":[282],"Extensive":[283],"comparative":[284],"experiments":[285],"various":[288,359],"datasets":[289],"demonstrates":[290],"superiority":[292],"over":[295],"terms":[300],"convergence":[302,316],"speed":[303,317],"generalization.":[306],"In":[307],"fact,":[308],"margin":[318,324],"5%":[320],"10%.":[326],"These":[327],"results":[328],"reveal":[329],"importance":[331],"need":[334],"capturing":[336],"context":[339],"learn":[341],"component":[344],"parameters.":[345],"proposed":[347],"harnesses":[349],"relationships,":[351],"therefore":[353],"enhances":[354],"learning":[356],"models":[357],"applications.":[360]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2024-02-07T00:00:00"}