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
- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Spring 2024
This work aims to address the lack of clear theoretical foundations in computational lexical semantics, the sub-field of natural language processing pertaining to computing with the meaning of words. Semantic tasks are of interest for end-user applications (e.g. contextual translation),...
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Spring 2021
Emphatic-Temporal-Difference (Emphatic-TD) learning algorithms were recently proposed based on the most central and widely used reinforcement learning algorithms, Temporal-Difference (TD) methods. Emphatic-TD learning algorithms were originally designed to solve the divergence problem of...
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Spring 2021
The backpropagation algorithm is a fundamental algorithm for training modern artificial neural networks (ANNs). However, it is known the backpropagation algorithm performs poorly on changing problems. We demonstrate the backpropagation algorithm can perform poorly on a clear, generic, changing...
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Toward Practical Reinforcement Learning Algorithms: Classification Based Policy Iteration and Model-Based Learning
DownloadSpring 2017
In this dissertation, we advance the theoretical understanding of two families of Reinforcement Learning (RL) methods: Classification-based policy iteration (CBPI) and model-based reinforcement learning (MBRL) with factored semi-linear models. In contrast to generalized policy iteration, CBPI...
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Fall 2018
Knowledge bases (KBs), repositories consisting of entities, facts about entities, and relations between entities, are a vital component for many tasks in artificial intelligence and natural language processing such as semantic search and question answering. Named Entity Disambiguation (NED), the...
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Spring 2023
Dialogue systems powered by large pre-trained language models exhibit an innate ability to deliver fluent and natural-sounding responses. Despite their impressive performance, these models fail to conduct interesting and consistent exchanges of turns and can often generate factually incorrect...
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Fall 2022
Medical Fake News is a pervasive part of the information that people consume on the internet. It may lead people to take actions which may put the lives of their family and community in danger - such actions include vaccine hesitancy, administering unverified and harmful treatments, etc. First...