{"id":"https://openalex.org/W7153993628","doi":"https://doi.org/10.48550/arxiv.2604.09288","title":"Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification","display_name":"Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7153993628","doi":"https://doi.org/10.48550/arxiv.2604.09288"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09288","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09288","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.2604.09288","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133514171","display_name":"Yilin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133507489","display_name":"Cai Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Cai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078343966","display_name":"Haishun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Haishun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129660914","display_name":"Ziyu Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Ziyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133522943","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0002-9648-4876"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Wei","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":0.7468000054359436,"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":0.7468000054359436,"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":0.06289999932050705,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.042899999767541885,"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/discriminative-model","display_name":"Discriminative model","score":0.48410001397132874},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46540001034736633},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.46399998664855957},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4424000084400177},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.4359999895095825},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.41690000891685486},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4018999934196472},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.36500000953674316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.656499981880188},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46540001034736633},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4571000039577484},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.444599986076355},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4424000084400177},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42910000681877136},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.41690000891685486},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.36059999465942383},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C11821877","wikidata":"https://www.wikidata.org/wiki/Q187959","display_name":"Distributive property","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26260000467300415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09288","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09288","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.2604.09288","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09288","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6752212643623352}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Trusted":[0,112],"multi-view":[1,144],"classification":[2],"typically":[3],"relies":[4],"on":[5],"a":[6,87,137,179],"view-wise":[7],"evidential":[8,174],"fusion":[9,93,125],"process:":[10],"each":[11],"view":[12],"independently":[13,68],"produces":[14],"class":[15],"evidence":[16,41,84,122,182],"and":[17,19,35,64,131,135,153,161,184],"uncertainty,":[18],"the":[20,89,142],"final":[21],"prediction":[22,75],"is":[23,33,45,53,189,196],"obtained":[24],"by":[25,97],"aggregating":[26],"these":[27],"independent":[28,173],"opinions.":[29],"While":[30],"this":[31,51,108],"design":[32],"modular":[34],"uncertainty-aware,":[36],"it":[37],"implicitly":[38],"assumes":[39],"that":[40,140],"from":[42,124],"different":[43],"views":[44,56],"numerically":[46],"comparable.":[47],"In":[48],"practice,":[49],"however,":[50],"assumption":[52],"fragile.":[54],"Different":[55],"often":[57],"differ":[58],"in":[59,83],"feature":[60],"space,":[61],"noise":[62],"level,":[63],"semantic":[65],"granularity,":[66],"while":[67],"trained":[69],"branches":[70],"are":[71],"optimized":[72],"only":[73],"for":[74,92],"correctness,":[76],"without":[77],"any":[78],"constraint":[79],"enforcing":[80],"cross-view":[81,181],"consistency":[82],"strength.":[85],"As":[86],"result,":[88],"uncertainty":[90],"used":[91],"can":[94],"be":[95],"dominated":[96],"branch-specific":[98],"scale":[99],"bias":[100],"rather":[101],"than":[102],"true":[103],"sample-level":[104,148],"reliability.":[105],"To":[106],"address":[107],"issue,":[109],"we":[110],"propose":[111],"Multi-view":[113],"learning":[114],"with":[115],"Unified":[116],"Routing":[117],"(TMUR),":[118],"which":[119],"decouples":[120],"view-specific":[121],"extraction":[123],"arbitration.":[126],"TMUR":[127],"uses":[128],"view-private":[129],"experts":[130],"one":[132],"collaborative":[133],"expert,":[134],"employs":[136],"unified":[138,186],"router":[139],"observes":[141],"global":[143,187],"context":[145],"to":[146,191],"generate":[147],"expert":[149,159,164],"weights.":[150],"Soft":[151],"load-balancing":[152],"diversity":[154],"regularization":[155],"further":[156],"encourage":[157],"balanced":[158],"utilization":[160],"more":[162],"discriminative":[163],"specialization.":[165],"We":[166],"also":[167],"provide":[168],"theoretical":[169],"analysis":[170],"showing":[171],"why":[172,185],"supervision":[175],"does":[176],"not":[177],"identify":[178],"common":[180],"scale,":[183],"routing":[188],"preferable":[190],"branch-local":[192],"arbitration":[193],"when":[194],"reliability":[195],"sample-dependent.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-14T00:00:00"}