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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
- 83Machine Learning
- 19Artificial Intelligence
- 17Reinforcement Learning
- 9Natural Language Processing
- 8Deep Learning
- 5Computer Vision
- 2Jacobsen, Andrew
- 2Wen, Junfeng
- 1Aghaei, Nikoo
- 1Alam Anik, Md Tanvir
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
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Fall 2023
The problem of missing data is omnipresent in a wide range of real-world datasets. When learning and predicting on this data with neural networks, the typical strategy is to fill-in or complete these missing values in the dataset, called impute-then-regress. Much less common is to attempt to...
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Spring 2019
In this thesis we introduce a new loss for regression, the Histogram Loss. There is some evidence that, in the problem of sequential decision making, estimating the full distribution of return offers a considerable gain in performance, even though only the mean of that distribution is used in...
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Fall 2019
Artificial agents have been shown to learn to communicate when needed to complete a cooperative task. Some level of language structure (e.g., compositionality) has been found in the learned communication protocols. This observed structure is often the result of specific environmental pressures...
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Fall 2024
This thesis studies a virtual power plant (VPP) that trades the bidirectional charging flexibility of privately owned plug-in electric vehicles (EVs) in a real-time electricity market to maximize its profit. The main contribution of this thesis is the development of scalable and efficient...
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Spring 2012
Recent advances in high-throughput technologies, such as genome-wide SNP analysis and microar- ray gene expression profiling, have led to a multitude of ranked lists, where the features (SNPs, genes) are sorted based on their individual correlation with a phenotype. Multiple reviews have shown...
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Fall 2022
Overfitting is a phenomenon when a machine learning system learns the patterns in training data so well that it starts to inauspiciously affect the model performance on unseen data. In practice, machine learning systems that overfit are not deployable rather systems that generalize well and do...
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Fall 2016
In this thesis, we investigate the move prediction problem in the game of Go by proposing a new ranking model named Factorization Bradley Terry (FBT) model. This new model considers the move prediction problem as group competitions while also taking the interaction between features into account....
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Fixed Point Propagation: A New Way To Train Recurrent Neural Networks Using Auxiliary Variables
DownloadFall 2019
Recurrent neural networks (RNNs), along with their many variants, provide a powerful tool for online prediction in partially observable problems. Two issues concerning RNNs, however, are the ability to capture long-term dependencies and long training times. There have been a variety of strategies...
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Spring 2016
Computer aided diagnosis of mental disorders like Attention Deficit Hyperactivity Disorder (ADHD) and Autism is a primary step towards automated detection and prognosis of these psychiatric diseases. This dissertation applies analyses based on learning models that use structural texture and...
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Spring 2023
Gradient Descent algorithms suffer many problems when learning representations using fixed neural network architectures, such as reduced plasticity on non-stationary continual tasks and difficulty training sparse architectures from scratch. A common workaround is continuously adapting the neural...