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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 7artificial intelligence
- 3machine learning
- 3reinforcement learning
- 2deep learning
- 1MTLR
- 1Uterine deformation
- 1Ady, Nadia Michelle
- 1Bennett, Brendan
- 1Chan, Alan
- 1Davis, Sarah (Sacha) Maren
- 1De Asis, Kris
- 1Ghosh, Shrimanti
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Spring 2020
Several Artificial Intelligence (AI) techniques such as machine learning, evolutionary computing, and Artificial Life (A-life) have been increasingly used to generate emergence of novel behaviours in multi-agent simulations (e.g., commercial games). However, automatically detecting emergent...
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Spring 2021
Temporal difference (TD) methods provide a powerful means of learning to make predictions in an online, model-free, and highly scalable manner. In the reinforcement learning (RL) framework, we formalize these prediction targets in terms of a (possibly discounted) sum of rewards, called the...
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Fall 2024
Value-based reinforcement learning is an approach to sequential decision making in which decisions are informed by learned, long-horizon predictions of future reward. This dissertation aims to understand issues that value-based methods face and develop algorithmic ideas to address these issues....
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Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
DownloadFall 2020
Policy gradient methods typically estimate both explicit policy and value functions. The long-extant view of policy gradient methods as approximate policy iteration---alternating between policy evaluation and policy improvement by greedification---is a helpful framework to elucidate algorithmic...
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Learning Individual Readmission-Free Survival Distributions using Longitudinal Medical Events
DownloadFall 2023
The rate of 30-day hospital readmission is a common measurement of hospital quality, which can affect the funding a hospital receives. Over a quarter of readmissions are estimated to be preventable with adequate interventions, but these interventions are themselves costly. For this reason, many...
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Predicting Uterine Deformation Due to Applicator Insertion in Pre-Brachytherapy MRI Using Deep Learning
DownloadSpring 2023
In locally advanced cervical cancer (LACC), brachytherapy (BT) remains the gold standard for boosting to curative doses in radiotherapy. Progress towards balancing target and routine tissue dosimetry for better clinical outcomes has been made possible by magnetic resonance imaging (MRI)-guided...
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Fall 2023
Curiosity appears to motivate and guide effective learning in humans, which has led to high hopes in the machine learning community for machine analogues of curiosity. While a variety of machine curiosity algorithms have been introduced, they are rarely compared with other existing curiosity...