{"id":"https://openalex.org/W4312072580","doi":"https://doi.org/10.1145/3568160.3570233","title":"Predictive Uncertainty Quantification of Deep Neural Networks using Dirichlet Distributions","display_name":"Predictive Uncertainty Quantification of Deep Neural Networks using Dirichlet Distributions","publication_year":2022,"publication_date":"2022-11-30","ids":{"openalex":"https://openalex.org/W4312072580","doi":"https://doi.org/10.1145/3568160.3570233"},"language":"en","primary_location":{"id":"doi:10.1145/3568160.3570233","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3568160.3570233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM Computer Science in Cars Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075770233","display_name":"Ahmed Hammam","orcid":"https://orcid.org/0000-0002-4110-2543"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Ahmed Hammam","raw_affiliation_strings":["Opel Automobile GmbH/ Stellantis N.V., Germany and Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems (KIT), Germany"],"affiliations":[{"raw_affiliation_string":"Opel Automobile GmbH/ Stellantis N.V., Germany and Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems (KIT), Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089224326","display_name":"Frank Bonarens","orcid":"https://orcid.org/0000-0003-3667-8211"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frank Bonarens","raw_affiliation_strings":["Opel Automobile GmbH/ Stellantis N.V., Germany"],"affiliations":[{"raw_affiliation_string":"Opel Automobile GmbH/ Stellantis N.V., Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046684906","display_name":"Seyed Eghbal Ghobadi","orcid":"https://orcid.org/0000-0002-2248-3811"},"institutions":[{"id":"https://openalex.org/I45155027","display_name":"Technische Hochschule Mittelhessen","ror":"https://ror.org/02qdc9985","country_code":"DE","type":"education","lineage":["https://openalex.org/I45155027"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Seyed Eghbal Ghobadi","raw_affiliation_strings":["Technische Hochschule Mittelhessen, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Hochschule Mittelhessen, Germany","institution_ids":["https://openalex.org/I45155027"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091574711","display_name":"Christoph Stiller","orcid":"https://orcid.org/0000-0003-4165-2075"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Stiller","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems (KIT), Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems (KIT), Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075770233"],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":0.398,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6776451,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987999796867371,"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":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.7529821395874023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6658352613449097},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6657677888870239},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.656018078327179},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.6218912601470947},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5841078758239746},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5576924085617065},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5504544377326965},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5401010513305664},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5309160351753235},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.4890902638435364},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48118335008621216},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45426151156425476},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41478103399276733},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33479565382003784},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11126738786697388},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09753569960594177}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7529821395874023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6658352613449097},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6657677888870239},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.656018078327179},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.6218912601470947},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5841078758239746},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5576924085617065},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5504544377326965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5401010513305664},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5309160351753235},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.4890902638435364},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48118335008621216},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45426151156425476},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41478103399276733},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33479565382003784},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11126738786697388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09753569960594177},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3568160.3570233","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3568160.3570233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM Computer Science in Cars Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1578800471","https://openalex.org/W2193145675","https://openalex.org/W2340897893","https://openalex.org/W2734024016","https://openalex.org/W2788633781","https://openalex.org/W2897313686","https://openalex.org/W2900954917","https://openalex.org/W2962700793","https://openalex.org/W2963163009","https://openalex.org/W2964309882","https://openalex.org/W2964339591","https://openalex.org/W2969825080","https://openalex.org/W3014641072","https://openalex.org/W3021332602","https://openalex.org/W3035478617","https://openalex.org/W3035711539","https://openalex.org/W3082897186","https://openalex.org/W3090027660","https://openalex.org/W3094466514","https://openalex.org/W3102100346","https://openalex.org/W3106250896","https://openalex.org/W3217335336"],"related_works":["https://openalex.org/W1991093342","https://openalex.org/W2078622645","https://openalex.org/W2055243143","https://openalex.org/W2170798819","https://openalex.org/W3127311823","https://openalex.org/W3196464345","https://openalex.org/W4321636575","https://openalex.org/W3094658433","https://openalex.org/W3042419602","https://openalex.org/W2966649771"],"abstract_inverted_index":{"Advancements":[0],"in":[1,29,58,92,122],"deep":[2,26,124],"neural":[3,27,125],"networks":[4,28],"have":[5],"made":[6],"it":[7],"a":[8,71,119],"prominent":[9],"approach":[10,47,73],"for":[11,22,112],"most":[12],"of":[13,25,61,77,89,98,115],"the":[14,23,54,87,96,103,109,113,123],"complex":[15],"computer":[16],"vision":[17],"tasks.":[18],"A":[19,44],"key":[20],"aspect":[21],"deployment":[24],"several":[30],"applications,":[31],"like":[32],"automotive":[33],"and":[34],"medical,":[35],"has":[36,68],"been":[37,70],"its":[38,42,133],"ability":[39],"to":[40,52,94],"estimate":[41],"uncertainty.":[43,62],"recent":[45],"leading":[46],"is":[48],"using":[49],"Dirichlet":[50],"distributions":[51],"model":[53],"uncertainty,":[55,78],"which":[56],"results":[57],"real-time":[59,75,104],"estimation":[60,76,100,128],"The":[63],"intermediate":[64],"layer":[65],"variational":[66],"inference":[67],"also":[69,131],"promising":[72],"to-enable":[74],"beating":[79],"state-of-the-art":[80],"approaches.":[81],"In":[82],"this":[83],"work":[84],"we":[85],"introduce":[86],"incorporation":[88],"both":[90],"approaches":[91],"order":[93],"improve":[95],"reliability":[97],"uncertainty":[99,127],"whilst":[101,130],"maintaining":[102],"capability.":[105],"Our":[106],"experiments":[107],"on":[108],"Cityscapes":[110],"dataset":[111],"task":[114],"semantic":[116],"segmentation":[117,134],"showed":[118],"significant":[120],"boost":[121],"network\u2019s":[126],"capability,":[129],"improving":[132],"performance.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}