{"id":"https://openalex.org/W7138875810","doi":"https://doi.org/10.48550/arxiv.2603.15656","title":"Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors","display_name":"Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors","publication_year":2026,"publication_date":"2026-03-08","ids":{"openalex":"https://openalex.org/W7138875810","doi":"https://doi.org/10.48550/arxiv.2603.15656"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15656","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.15656","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014821337","display_name":"Peiyu Yang","orcid":"https://orcid.org/0000-0002-3827-8476"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Peiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130123412","display_name":"Naveed Akhtar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akhtar, Naveed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130163956","display_name":"Jiantong Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jiantong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129870136","display_name":"Ajmal Mian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mian, Ajmal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8960000276565552,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8960000276565552,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.03200000151991844,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.019600000232458115,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rectification","display_name":"Rectification","score":0.8518999814987183},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.8222000002861023},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.8009999990463257},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7524999976158142},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6592000126838684},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5209000110626221},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.45339998602867126}],"concepts":[{"id":"https://openalex.org/C50942859","wikidata":"https://www.wikidata.org/wiki/Q4967193","display_name":"Rectification","level":3,"score":0.8518999814987183},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.8222000002861023},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.8009999990463257},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7524999976158142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746399998664856},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6592000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5619999766349792},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5209000110626221},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.45339998602867126},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3903999924659729},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.32600000500679016},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2985999882221222},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26269999146461487},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15656","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.15656","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15656","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"performance":[1,87,161],"of":[2,15,94,102,116,142],"neural":[3,150],"network":[4],"models":[5,24],"deteriorates":[6],"due":[7],"to":[8,19,25,52],"their":[9,20],"unreliable":[10,65,82],"behavior":[11,83],"on":[12,91],"non-robust":[13],"features":[14],"corrupted":[16],"samples.":[17,96],"Owing":[18],"opaque":[21],"nature,":[22],"rectifying":[23,104],"address":[26],"this":[27,45],"problem":[28],"often":[29],"necessitates":[30],"arduous":[31],"data":[32],"cleaning":[33],"and":[34,41,62,88,129,154],"model":[35,50,56,64,75,86,103],"retraining,":[36],"resulting":[37],"in":[38,145],"huge":[39],"computational":[40],"manual":[42],"overhead.":[43],"In":[44],"work,":[46],"we":[47,118],"leverage":[48],"rank-one":[49],"editing":[51,165],"establish":[53],"an":[54,120],"attribution-guided":[55,121],"rectification":[57,71],"framework":[58],"that":[59,80,125],"effectively":[60],"locates":[61],"corrects":[63,81],"behaviors.":[66],"We":[67,97],"first":[68],"distinguish":[69],"our":[70,143],"setting":[72],"from":[73,106],"existing":[74],"editing,":[76],"yielding":[77],"a":[78,100,171],"formulation":[79],"while":[84],"preserving":[85],"reducing":[89],"reliance":[90],"large":[92],"budgets":[93],"cleansed":[95,173],"further":[98],"reveal":[99],"bottleneck":[101],"arising":[105],"heterogeneous":[107],"editability":[108,128],"across":[109],"layers.":[110],"To":[111],"target":[112],"the":[113,131,140],"primary":[114],"source":[115],"misbehavior,":[117],"introduce":[119],"layer":[122,132],"localization":[123],"method":[124,144,158],"quantifies":[126],"layer-wise":[127],"identifies":[130],"most":[133],"responsible":[134],"for":[135,149,179],"unreliabilities.":[136],"Extensive":[137],"experiments":[138],"demonstrate":[139],"effectiveness":[141],"correcting":[146],"unreliabilities":[147],"observed":[148],"Trojans,":[151],"spurious":[152],"correlations":[153],"feature":[155],"leakage.":[156],"Our":[157],"shows":[159],"remarkable":[160],"by":[162],"achieving":[163],"its":[164],"objective":[166],"with":[167],"as":[168,170],"few":[169],"single":[172],"sample,":[174],"which":[175],"makes":[176],"it":[177],"appealing":[178],"practice.":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-20T00:00:00"}