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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
- 3Interpretability
- 1Concept Discovery
- 1Deep Interpretable Modelling
- 1Explainable AI
- 1Fuzzy Neural Netowrks
- 1Knowledge Extraction
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Spring 2023
Babiker, Housam Khalifa Bashier
The recent success of deep neural networks has exposed the problem of model transparency. The need for explainability is particularly critical in sensitive domains. In addition, regulatory frameworks for the “responsible” deployment of AI are emerging, creating legal requirements for transparent,...
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Fall 2024
Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of these policies, likely because of the complexity of such an evaluation. In this dissertation, we introduce a novel...
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Logic-Oriented Fuzzy Neural Networks: Optimization and Applications of Interpretable Models of Machine Learning
DownloadFall 2023
With the rapid development of machine learning models along with increasingly complex data structures, it becomes difficult to ground the reliability of models’ predictions despite the substantial progress in favor of high approximation properties. The lack of interpretability remains a key...