Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/113612
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorShen, Chunhua-
dc.contributor.advisorReid, Ian-
dc.contributor.authorZhuang, Bohan-
dc.date.issued2018-
dc.identifier.urihttp://hdl.handle.net/2440/113612-
dc.description.abstractThe 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.subjectResearch by publicationen
dc.subjectdeep learningen
dc.subjectenergy-efficient neural networksen
dc.subjecthashingen
dc.subjectrelationship detectionen
dc.titleTowards efficient deep neural networks with applications to visual recognitionen
dc.typeThesesen
dc.contributor.schoolSchool of Computer Scienceen
dc.provenanceThis 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/legalsen
dc.description.dissertationThesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018en
Appears in Collections:Research Theses

Files in This Item:
File Description SizeFormat 
Zhuang2018_PhD.pdf27.92 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.