Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

Conference item


Saleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq 2020. Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks. IEEE. https://doi.org/10.1109/ucc48980.2020.00070
AuthorsSaleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq
Abstract

Artificial 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.

KeywordsArtificial Intelligence; Deep Neural Networks; Partial differential equations; Discretization
Year2020
Journal2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/ucc48980.2020.00070
Web address (URL)http://hdl.handle.net/10545/625606
http://creativecommons.org/licenses/by-nc-sa/4.0/
hdl:10545/625606
ISBN9780738123943
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Publication dates30 Dec 2020
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Deposited08 Feb 2021, 15:50
Accepted30 Oct 2020
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Attribution-NonCommercial-ShareAlike 4.0 International

ContributorsUniversity of Derby and University of Leicester
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Yaseen, M., Zafar, Muhammad Sarim, Anjum, Ashiq and Hill, Richard 2016. High performance video processing in cloud data centres. IEEE. https://doi.org/10.1109/SOSE.2016.56
An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: architecture and evaluation
Sotiriadis, Stelios, Bessis, Nik, Anjum, Ashiq and Buyya, Rajkumar 2015. An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: architecture and evaluation. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2015.2399312
Glueing grids and clouds together: a service-oriented approach
Anjum, Ashiq, Hill, Richard, McClatchey, Richard, Bessis, Nik and Branson, Andrew 2012. Glueing grids and clouds together: a service-oriented approach. International Journal of Web and Grid Services. https://doi.org/10.1504/IJWGS.2012.049169
Energy conservation in mobile devices and applications: a case for context parsing, processing and distribution in clouds
Kiani, Saad Liaquat, Anjum, Ashiq, Bessis, Nik, Hill, Richard and Knappmeyer, Michael 2013. Energy conservation in mobile devices and applications: a case for context parsing, processing and distribution in clouds. Mobile Information Systems.
Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework
Ikram, Ahsan, Anjum, Ashiq, Hill, Richard, Antonopoulos, Nikolaos, Liu, Lu and Sotiriadis, Stelios 2013. Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework. Concurrency and Computation: Practice and Experience. https://doi.org/10.1002/cpe.3131
Performance simulation of a context provisioning middleware based on empirical measurements
Reetz, Eike Steffen, Knappmeyer, Michael, Kiani, Saad Liaquat, Anjum, Ashiq, Bessis, Nik and Tönjes, Ralf 2012. Performance simulation of a context provisioning middleware based on empirical measurements. https://doi.org/10.1016/j.simpat.2012.03.002
Dot-base62x: building a compact and user-friendly text representation scheme of ipv6 addresses for cloud computing
Liu, Zhenxing, Liu, Lu, Hardy, J., Anjum, Ashiq, Hill, Richard and Antonopoulos, Nikolaos 2012. Dot-base62x: building a compact and user-friendly text representation scheme of ipv6 addresses for cloud computing. Journal of Cloud Computing: Advances, Systems and Applications. https://doi.org/10.1186/2192-113X-1-3