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Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing
Dissertation (MSc)--University of Pretoria, 2018.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2019
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| _version_ | 1867613542977372160 |
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| access_status_str | Open Access |
| author2 | De Waal, Alta |
| author_browse | De Waal, Alta |
| author_facet | De Waal, Alta |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/68726 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:48.825Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/68726 Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing De Waal, Alta steyncarl@gmail.com Steyn, Carl Mathematical Statistics UCTD Dissertation (MSc)--University of Pretoria, 2018. Neural Networks (NNs) play an integral role in modern machine learning development. Recent advances in NN research have led to a wide array of applications, ranging from medical diagnosis [1] to complex problems such as facial and object recognition [2] [3]. However, despite the increasingly powerful predictive capabilities of NNs, some limitations exist which could cause more traditional methods to become the preferred alternative. Most of these limitations result from the "black box" nature of the NN in which the estimated model parameters are not interpretable. The output of traditional NNs also contain no measure of uncertainty in its predictions, causing decision-making to become challenging when NN output plays an important role such as in automatic medical imaging and autonomous vehicles. To address these challenges, we investigate a probabilistic approach to NNs through Bayesian inference and discuss di erent methods in approximating the posterior distributions of NN parameters. We investigate results when extending the NN structure to deeper architectures such as Convolutional Neural Networks and discuss the advantage of extracting additional information from the posterior predictive distribution to measure prediction uncertainty. NRF Statistics MSc Unrestricted 2019-04-01T07:37:45Z 2019-04-01T07:37:45Z 2019 2018 Dissertation Steyn, C 2018, Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/68726> http://hdl.handle.net/2263/68726 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Mathematical Statistics UCTD Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| title | Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| title_full | Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| title_fullStr | Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| title_full_unstemmed | Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| title_short | Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| title_sort | bayesian convolutional neural networks a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing |
| topic | Mathematical Statistics UCTD |
| url | http://hdl.handle.net/2263/68726 |