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Thesis

Exploiting prior knowledge in machine learning model design

Abstract:

The design of models which are appropriate for specific tasks is an important activity in machine learning. This thesis considers multiple ways in which knowledge about the task at hand can be incorporated into the design of a machine learning model: (i) by using Bayesian models, which incorporate prior knowledge in probability distributions; (ii) by designing models to respect the symmetries of the task; (iii) by adapting models in a practical setting. For Bayesian models, we propose a pr...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Research group:
Bayesian Exploration Lab
Oxford college:
Worcester College
Role:
Author

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Name:
Engineering and Physical Sciences Research Council
Funding agency for:
Wagstaff, E
Programme:
EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
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Deposit date:
2022-01-15

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