{"id":"https://openalex.org/W7162130000","doi":"https://doi.org/10.48550/arxiv.2605.22746","title":"Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier","display_name":"Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162130000","doi":"https://doi.org/10.48550/arxiv.2605.22746"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22746","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22746","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.22746","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136805462","display_name":"Berk Hayta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hayta, Berk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092897307","display_name":"Hannah Laus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laus, Hannah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136732211","display_name":"Simon Mittermaier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mittermaier, Simon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090264479","display_name":"Felix Krahmer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krahmer, Felix","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.3025999963283539,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.3025999963283539,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.16760000586509705,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0908999964594841,"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/softmax-function","display_name":"Softmax function","score":0.9265999794006348},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5741000175476074},{"id":"https://openalex.org/keywords/empirical-risk-minimization","display_name":"Empirical risk minimization","score":0.5318999886512756},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5260000228881836},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.43709999322891235},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.3944000005722046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3864000141620636}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.9265999794006348},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305999755859375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6243000030517578},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5701000094413757},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.5318999886512756},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5260000228881836},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.43709999322891235},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3476000130176544},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.3398999869823456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3301999866962433},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2542000114917755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22746","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22746","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.22746","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22746","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"sensor-based":[1],"learning":[2,60,187],"systems":[3],"require":[4],"uncertainty":[5,20,137],"estimation":[6,21],"that":[7,166],"is":[8,201],"both":[9],"reliable":[10],"and":[11,65,94,117,164,171,189],"computationally":[12],"efficient.":[13],"Evidential":[14],"Deep":[15],"Learning":[16],"(EDL)":[17],"provides":[18,126],"single-pass":[19],"by":[22,36,71,83],"modeling":[23],"the":[24,31,73,76,91,100,129,134,148,154,159,193,202],"class":[25,110],"probabilities":[26],"via":[27],"Dirichlet":[28,32,51,92],"distributions,":[29],"where":[30],"parameters":[33],"are":[34,54],"predicted":[35],"a":[37,86,108,121,141],"learned":[38],"neural":[39],"network":[40],"mapping.":[41],"However,":[42],"this":[43,69,198],"approach":[44],"can":[45],"lead":[46],"to":[47,176,182,204],"computational":[48],"challenges,":[49],"as":[50],"expected":[52],"objectives":[53,157],"more":[55],"complex":[56],"than":[57],"standard":[58,149,185],"supervised":[59],"losses,":[61],"complicating":[62],"their":[63],"analysis":[64,125,200],"implementation.":[66],"We":[67,152],"address":[68],"issue":[70],"approximating":[72],"objective":[74],"of":[75,111,131,136,195],"first-order":[77],"empirical":[78,199],"risk":[79],"minimization":[80],"problem":[81],"induced":[82],"EDL":[84],"with":[85,104],"plug-in":[87],"loss":[88,112],"evaluated":[89],"at":[90],"mean":[93],"show":[95,165],"that,":[96],"under":[97,140],"mild":[98],"assumptions,":[99],"approximation":[101],"error":[102,116],"decays":[103],"growing":[105],"evidence":[106],"for":[107,128,208],"broad":[109],"functions,":[113],"including":[114],"mean-squared":[115],"cross-entropy":[118],"loss.":[119],"As":[120],"special":[122],"case,":[123],"our":[124,145,196],"justification":[127],"use":[130],"softmax":[132,150],"in":[133],"context":[135],"estimation,":[138],"since":[139],"particular":[142],"evidence-to-Dirichlet":[143],"mapping,":[144],"framework":[146],"includes":[147],"classifier.":[151],"validate":[153],"proposed":[155],"simplified":[156],"on":[158],"Google":[160],"Speech":[161],"Commands":[162],"dataset":[163],"they":[167],"achieve":[168],"predictive":[169],"accuracy":[170],"selective":[172],"prediction":[173],"performance":[174],"comparable":[175],"classical":[177],"EDL,":[178],"while":[179],"being":[180],"simpler":[181],"implement":[183],"using":[184],"deep":[186],"losses":[188],"training":[190],"pipelines.":[191],"To":[192],"best":[194],"knowledge,":[197],"first":[203],"obtain":[205],"coverage-accuracy":[206],"trade-offs":[207],"speech":[209],"recognition":[210],"tasks":[211],"through":[212],"EDL.":[213]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}
