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dc.contributor.authorSaleem, Rabia
dc.contributor.authorYuan, Bo
dc.contributor.authorKurugollu, Fatih
dc.contributor.authorAnjum, Ashiq
dc.date.accessioned2021-02-08T15:50:56Z
dc.date.available2021-02-08T15:50:56Z
dc.date.issued2020-12-30
dc.identifier.citationSaleem, R., Yuan, B., Kurugollu, F. and Anjum, A. (2020). 'Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks'. IEEE/ACM 13th International Conference on Utility and Cloud Computing, Leicester, 7-10 December. New York: IEEE, pp. 446-448.en_US
dc.identifier.isbn9780738123943
dc.identifier.doi10.1109/ucc48980.2020.00070
dc.identifier.urihttp://hdl.handle.net/10545/625606
dc.description.abstractArtificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decisionmaking is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/abstract/document/9302808en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.source2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
dc.subjectArtificial Intelligenceen_US
dc.subjectDeep Neural Networksen_US
dc.subjectPartial differential equationsen_US
dc.subjectDiscretizationen_US
dc.titleExplaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networksen_US
dc.typeMeetings and Proceedingsen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Leicesteren_US
dc.identifier.journal2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)en_US
dcterms.dateAccepted2020-10-30
dc.author.detailSTF1867en_US


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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International