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Thesis

Understanding convolutional neural networks

Abstract:

In the past decade, deep learning has fueled a number of exciting developments in artificial intelligence (AI). However, as deep learning is increasingly being applied to high-impact domains, like medical diagnosis or autonomous driving, the impact of its failures also increases. Because of their high complexity (i.e. they are typically composed of millions of parameters), deep learning models are difficult to interpret. Thus, there is a great need for tools that help us understand how su...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Oxford college:
St John's College
Role:
Author
ORCID:
0000-0001-8831-6402

Contributors

Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Oxford college:
New College
Role:
Supervisor
ORCID:
0000-0003-1374-2858
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Research group:
Visual Geometry Group
Role:
Examiner
ORCID:
0000-0002-8945-8573
Institution:
Massachusetts Institute of Technology
Role:
Examiner
ORCID:
0000-0003-4915-0256
More from this funder
Programme:
Rhodes Scholarship
Funding agency for:
Fong, R
More from this funder
Programme:
Open Phil AI Fellowship
Funding agency for:
Fong, R
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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