Uncertainty Quantification Neural Network from Similarity and Sensitivity Dipu Kabir, HM, Khosravi, Abbas, Nahavandi, Darius and Nahavandi, Saeid 2020, Uncertainty Quantification Neural Network from Similarity and Sensitivity, in IEEE IJCNN 2020: Proceedings of the International Joint Conference on Neural Networks, IEEE Institute Electrical Electronics Engineers, Piscataway, N.J., pp. 1-8, doi: 10.1109/IJCNN48605.2020.9206746. Attached Files Name Description MIMEType Size Downloads Title Uncertainty Quantification Neural Network from Similarity and Sensitivity Author(s) Dipu Kabir, HM Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Darius orcid.org/0000-0002-5007-9584 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name IEEE IJCNN Neural Networks. International Joint Conference (2020: Glasgow, Eng.) Conference location Glasgow, Eng. Conference dates 2020/07/19 - 2020/07/24 Title of proceedings IEEE IJCNN 2020: Proceedings of the International Joint Conference on Neural Networks Publication date 2020-07-01 Start page 1 End page 8 Total pages 8 Publisher IEEE Institute Electrical Electronics Engineers Place of publication Piscataway, N.J. Keyword(s) Uncertainty Bound Probabilistic Forecast Neural Network Prediction Interval Uncertainty Quantification Het- eroscedastic Uncertainty Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Hardware & Architecture Computer Science Heteroscedastic Uncertainty INTEGRATION ISBN 9781728169262 Language eng DOI 10.1109/IJCNN48605.2020.9206746 HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30145245 Document type: Conference Paper Collection: Institute for Intelligent Systems Research and Innovation (IISRI) Related Links Link Description Connect to published version 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. Versions Version Filter Type Thu, 12 Nov 2020, 09:02:34 EST Thu, 12 Nov 2020, 10:37:09 EST Fri, 13 Nov 2020, 04:01:38 EST Fri, 13 Nov 2020, 06:57:09 EST Sat, 17 Apr 2021, 02:30:30 EST Thu, 07 Jul 2022, 15:35:24 EST Mon, 18 Jul 2022, 07:38:22 EST Mon, 18 Jul 2022, 12:45:02 EST Mon, 18 Jul 2022, 14:36:02 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 15 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 12 Nov 2020, 10:37:09 EST