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https://hdl.handle.net/2440/113612
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DC Field | Value | Language |
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dc.contributor.advisor | Shen, Chunhua | - |
dc.contributor.advisor | Reid, Ian | - |
dc.contributor.author | Zhuang, Bohan | - |
dc.date.issued | 2018 | - |
dc.identifier.uri | http://hdl.handle.net/2440/113612 | - |
dc.description.abstract | The thesis focuses on the following two topics: designing energy-efficient neural networks and hashing approach to make deep learning more feasible to real applications; deep convolutional neural networks for visual recognition. | en |
dc.subject | Research by publication | en |
dc.subject | deep learning | en |
dc.subject | energy-efficient neural networks | en |
dc.subject | hashing | en |
dc.subject | relationship detection | en |
dc.title | Towards efficient deep neural networks with applications to visual recognition | en |
dc.type | Theses | en |
dc.contributor.school | School of Computer Science | en |
dc.provenance | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals | en |
dc.description.dissertation | Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018 | en |
Appears in Collections: | Research Theses |
Files in This Item:
File | Description | Size | Format | |
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Zhuang2018_PhD.pdf | 27.92 MB | Adobe PDF | View/Open |
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