{"id":"https://openalex.org/W3203288787","doi":null,"title":"A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation.","display_name":"A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation.","publication_year":2021,"publication_date":"2021-10-06","ids":{"openalex":"https://openalex.org/W3203288787","mag":"3203288787"},"language":"en","primary_location":{"id":"mag:3203288787","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2110.03051.pdf","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2110.03051.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001376704","display_name":"Dennis Ulmer","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dennis Ulmer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5001376704"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"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":0.8119999766349792,"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":0.8119999766349792,"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":0.09260000288486481,"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":0.029999999329447746,"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/evidential-deep-learning","display_name":"evidential deep learning","score":0.9940000176429749},{"id":"https://openalex.org/keywords/uncertainty-estimation","display_name":"uncertainty estimation","score":0.800000011920929},{"id":"https://openalex.org/keywords/forecast-uncertainty","display_name":"forecast uncertainty","score":0.6119999885559082},{"id":"https://openalex.org/keywords/classification","display_name":"classification","score":0.5619999766349792},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"regression analysis","score":0.984000027179718},{"id":"https://openalex.org/keywords/bayesian-neural-networks","display_name":"Bayesian neural networks","score":0.6710000038146973},{"id":"https://openalex.org/keywords/monte-carlo-dropout","display_name":"Monte Carlo dropout","score":0.6460000276565552},{"id":"https://openalex.org/keywords/ensembling","display_name":"ensembling","score":0.6600000262260437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7479028105735779},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6599401831626892},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6443232297897339},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6030557155609131},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5081722736358643},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49262839555740356},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4707418382167816},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.46113741397857666},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.45864608883857727},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.44955524802207947},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.4140096604824066},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38156697154045105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12909185886383057},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10882726311683655},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3203288787","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2110.03051.pdf","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3203288787","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2110.03051.pdf","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1516111018","https://openalex.org/W1959608418","https://openalex.org/W1979815131","https://openalex.org/W2026653933","https://openalex.org/W2045656233","https://openalex.org/W2111959010","https://openalex.org/W2460937040","https://openalex.org/W2531327146","https://openalex.org/W2560321925","https://openalex.org/W2626967530","https://openalex.org/W2767443199","https://openalex.org/W2790871512","https://openalex.org/W2806471870","https://openalex.org/W2867167548","https://openalex.org/W2896687685","https://openalex.org/W2905485203","https://openalex.org/W2905813690","https://openalex.org/W2948194985","https://openalex.org/W2951266961","https://openalex.org/W2963090522","https://openalex.org/W2963693742","https://openalex.org/W2964317943","https://openalex.org/W2965189679","https://openalex.org/W2970115835","https://openalex.org/W2970961383","https://openalex.org/W2977360582","https://openalex.org/W2977983631","https://openalex.org/W2979338472","https://openalex.org/W2987179096","https://openalex.org/W2992525328","https://openalex.org/W2995085126","https://openalex.org/W2996184071","https://openalex.org/W3007329367","https://openalex.org/W3009086677","https://openalex.org/W3014596384","https://openalex.org/W3034669169","https://openalex.org/W3036729765","https://openalex.org/W3037939899","https://openalex.org/W3093038536","https://openalex.org/W3094749184","https://openalex.org/W3097528668","https://openalex.org/W3098801583","https://openalex.org/W3100334809","https://openalex.org/W3100354738","https://openalex.org/W3100691980","https://openalex.org/W3104278118","https://openalex.org/W3106195168","https://openalex.org/W3131119906","https://openalex.org/W3134514250","https://openalex.org/W3159054876","https://openalex.org/W3163155043","https://openalex.org/W3167724130","https://openalex.org/W3168394938","https://openalex.org/W3172266654","https://openalex.org/W3176678050","https://openalex.org/W3183398589","https://openalex.org/W3186734958"],"related_works":["https://openalex.org/W7103308507","https://openalex.org/W4415795848","https://openalex.org/W2971196730","https://openalex.org/W4415554422","https://openalex.org/W6950425519","https://openalex.org/W4416056028","https://openalex.org/W4404110280","https://openalex.org/W4382317663","https://openalex.org/W3154908283","https://openalex.org/W3132339071"],"abstract_inverted_index":{"Popular":[0],"approaches":[1,137],"for":[2,18],"quantifying":[3],"predictive":[4],"uncertainty":[5,85],"in":[6,87,108,149],"deep":[7],"neural":[8],"networks":[9],"often":[10],"involve":[11],"a":[12,79,88,109,127],"set":[13],"of":[14,56,62,117,134],"weights":[15],"or":[16,22,38],"models,":[17],"instance":[19],"via":[20],"ensembling":[21],"Monte":[23],"Carlo":[24],"Dropout.":[25],"These":[26],"techniques":[27],"usually":[28],"produce":[29,41],"overhead":[30],"by":[31,94],"having":[32],"to":[33,48,121,139,151],"train":[34],"multiple":[35],"model":[36,90],"instances":[37],"do":[39],"not":[40],"very":[42],"diverse":[43],"predictions.":[44],"This":[45,99],"survey":[46,100,114],"aims":[47],"familiarize":[49],"the":[50,60,106,115,118,130,135,144],"reader":[51],"with":[52],"an":[53],"alternative":[54],"class":[55],"models":[57],"based":[58],"on":[59,105,129],"concept":[61],"Evidential":[63],"Deep":[64],"Learning:":[65],"For":[66],"unfamiliar":[67],"data,":[68],"they":[69,72,83],"admit":[70],"what":[71],"don't":[73],"know":[74],"and":[75,91,132,142],"fall":[76],"back":[77],"onto":[78],"prior":[80],"belief.":[81],"Furthermore,":[82],"allow":[84],"estimation":[86],"single":[89],"forward":[92],"pass":[93],"parameterizing":[95],"distributions":[96],"over":[97],"distributions.":[98],"recapitulates":[101],"existing":[102,140],"works,":[103],"focusing":[104],"implementation":[107],"classification":[110],"setting.":[111],"Finally,":[112],"we":[113],"application":[116],"same":[119],"paradigm":[120],"regression":[122],"problems.":[123],"We":[124],"also":[125],"provide":[126,143],"reflection":[128],"strengths":[131],"weaknesses":[133],"mentioned":[136],"compared":[138],"ones":[141],"most":[145],"central":[146],"theoretical":[147],"results":[148],"order":[150],"inform":[152],"future":[153],"research.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-10-11T00:00:00"}
