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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
- 3Transfer Learning
- 1Artificial Intelligence
- 1Deep Learning
- 1Generative AI
- 1Iranian Music
- 1Long Short-term Memory
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Fall 2023
Deep learning has had much success on challenging problems with large datasets. However, it struggles in cases with limited training data. Transfer learning represents a class of approaches for transferring knowledge from large source datasets to smaller target datasets. But many transfer...
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Fall 2023
Deep learning approaches have had success in many domains recently, particularly in domains with large amounts of training data. However, there are domains without a sufficient quantity of training data, or where the training data present is of insufficient quality. Transfer learning approaches...
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Fall 2023
The increasing popularity of Deep Neural Networks (DNN) has led to their application to many domains, including Music Generation. However, these large DNN-based models are heavily dependent on their training dataset, which means they perform poorly on musical genres that are out-of-distribution...