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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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RELIABLE WIRELESS SENSOR NETWORKS USING MULTIPLE SINKS AND DEGREE CONSTRAINED SHORTEST PATH TREES
DownloadSpring 2013
Wireless Sensor networks (WSN) have gained attention in both industry and academia due to their versatile fields of application. One of the main characteristics of a sensor node is its limited energy supply. The network needs to be reliable in the sense that it can deliver the data to sink with...
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Fall 2024
Classical wisdom in machine learning advises controlling the complexity of the hypothesis space for achieving good generalization. Despite this, modern overparametrized neural networks demonstrate remarkably high generalization performance, oftentimes with larger and more expressive architectures...
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Fall 2022
The rise of Deep Learning (DL) and its assistance in learning complex feature representations significantly impacted Reinforcement Learning (RL). Deep Reinforcement Learning (DRL) made it possible to apply RL to complex real-world problems and even achieve human-level performance. One of these...
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Fall 2020
Research in artificial general intelligence aims to create agents that can learn from their own experience to solve arbitrary tasks in complex and dynamic settings. To do so effectively and efficiently, such an agent must be able to predict how its environment will change both dependently and...