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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
- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Fall 2021
Learning auxiliary tasks, such as multiple predictions about the world, can provide many bene ts to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather...
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Fall 2023
Multilevel action selection is a reinforcement learning technique in which an action is broken into two parts, the type and the parameters. When using multilevel action selection in reinforcement learning, one must break the action space into multiple subsets. These subsets are typically disjoint...
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Fall 2009
Group comparison per se is a fundamental task in many scientific endeavours but is also the basis of any classifier. Comparing groups of sequence data is a relevant task. To contrast sequence groups, we define Emerging Sequences (ESs) as subsequences that are frequent in sequences of one group...
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Fall 2024
This thesis presents a novel data-driven approach for identifying categoryselective regions in the human brain that are consistent across multiple participants. By leveraging a massive fMRI dataset and a multi-modal (language and image) neural network (CLIP), we trained a highly accurate...
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Fall 2020
Conversational agents, also known as chatbots, are designed to have a real-time conversation with humans. Closed domain chatbots are limited to a specific task they’re designed to do. They can be rule-based or information retrieval based chatbots while open domain chatbots are meant to mimic...
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Spring 2017
Most machine learning problems can be posed as solving a mathematical program that describes the structure of the prediction problem, usually expressed in terms of carefully chosen losses and regularizers. However, many machine learning problems yield mathematical programs that are not convex in...
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Fall 2016
This thesis explores theoretical, computational, and practical aspects of convex (shape-constrained) regression, providing new excess risk upper bounds, a comparison of convex regression techniques with theoretical guarantee, a novel heuristic training algorithm for max-affine representations,...
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Fall 2023
A survival dataset describes a collection of instances, such as patients, and associates each instance with either the time until an event (such as death), or the censoring time (eg, when the instance is lost to follow-up), which is a lower bound on the time until the event. While there are...