A review of uncertainty quantification in deep learning: Techniques, applications and challenges Abdar, Moloud, Pourpanah, F, Hussain, S, Rezazadegan, D, Liu, L, Ghavamzadeh, M, Fieguth, P, Cao, X, Khosravi, Abbas, Acharya, UR, Makarenkov, V and Nahavandi, Saeid 2021, A review of uncertainty quantification in deep learning: Techniques, applications and challenges, Information Fusion, vol. 76, pp. 243-297, doi: 10.1016/j.inffus.2021.05.008. Attached Files Name Description MIMEType Size Downloads Title A review of uncertainty quantification in deep learning: Techniques, applications and challenges Author(s) Abdar, Moloud orcid.org/0000-0002-3059-6357 Pourpanah, F Hussain, S Rezazadegan, D Liu, L Ghavamzadeh, M Fieguth, P Cao, X Khosravi, Abbas orcid.org/0000-0001-6927-0744 Acharya, UR Makarenkov, V Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Information Fusion Volume number 76 Start page 243 End page 297 Total pages 55 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2021 ISSN 1566-2535 1872-6305 Keyword(s) Artificial intelligence Bayesian statistics Computer Science Computer Science, Artificial Intelligence Computer Science, Theory & Methods Deep learning DYNAMICS Ensemble learning INFORMATION MACHINE Machine learning MEDICAL IMAGE SEGMENTATION MODEL MOLECULAR-PROPERTIES NEURAL-NETWORKS PREDICTION Science & Technology Technology Uncertainty quantification VARIANCE VARIATIONAL INFERENCE Language eng DOI 10.1016/j.inffus.2021.05.008 Field of Research 0801 Artificial Intelligence and Image Processing HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30152127 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection Related Links Link Description Link to full-text (Open access) Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 04 Jun 2021, 08:22:33 EST Sat, 05 Jun 2021, 05:01:45 EST Mon, 07 Jun 2021, 08:22:21 EST Sat, 03 Jul 2021, 02:32:06 EST Sat, 18 Sep 2021, 02:30:36 EST Wed, 02 Feb 2022, 05:02:39 EST Wed, 09 Mar 2022, 15:11:49 EST Wed, 09 Mar 2022, 16:07:17 EST Wed, 09 Mar 2022, 16:16:59 EST Filtered Full Citation counts:   Cited 75 times in TR Web of Science Cited 96 times in Scopus Search Google Scholar Access Statistics: 44 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 04 Jun 2021, 08:22:33 EST