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Thesis

Improving representation learning through variational autoencoding

Abstract:

Representation learning aims to distill useful knowledge from raw data and apply this knowledge to a wide range of applications. This ability to extract information that is useful not only for selected tasks but also generalizes to new settings is a key step towards artificial intelligence.

In this thesis, we focus on representations derived through a specific type of generative model, i.e. variational autoencoders (VAEs). VAEs have several desirable properties. Thanks to the use ...

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Division:
MPLS
Department:
Computer Science
Role:
Author

Contributors

Role:
Supervisor
Role:
Supervisor
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/L015897/1
Programme:
AIMS CDT studentship in partner with ABB
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Name:
China Scholarship Council
Funder identifier:
http://dx.doi.org/10.13039/501100004543
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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