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- 26Artificial Intelligence
- 23Reinforcement Learning
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- 165Graduate and Postdoctoral Studies (GPS), Faculty of
- 165Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 8Computing Science, Department of
- 7Computing Science, Department of/Technical Reports (Computing Science)
- 2Chemical and Materials Engineering, Department of
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- 165Thesis
- 8Report
- 5Article (Published)
- 2Conference/Workshop Poster
- 2Research Material
- 1Article (Draft / Submitted)
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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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Fall 2024
Operated under changing wind speed and harsh environment conditions, the rotating parts in wind turbine gearboxes, such as gears and bearings, will deteriorate and become faulty over time. By conducting real-time and accurate fault detection and diagnosis before significant failures occur, we can...
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Fall 2019
Data is becoming more valuable as there are still many uncertainties and hidden information that have yet to be discovered. For this reason, the application of data analysis and machine learning in the industry is becoming more popular. For example, SAGD (steam assisted gravity drainage) is a...
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Fall 2021
Convolutional Neural Networks (CNNs) have been recently seeing great success in various image classification fields and applications. However, this success has been accompanied by a significant increase in memory and computational demands, limiting their use in resource-limited devices, e.g.,...
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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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Fall 2017
This thesis examines the predictability of Canadian recessions with special emphasis on variable selection in a big data environment. The first paper in this thesis addresses the problem of variable selection from a traditional point of view by employing a prescreened set of selected individual...
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Spring 2024
This thesis aims to create a platform to estimate and monitor the University of Alberta (UAlberta) fleet vehicles’ fuel consumption and Carbon Dioxide (CO2) emissions. The main objective is to collect and analyze fleet vehicles information to reduce energy consumption and greenhouse gas emissions...
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Spring 2019
The extraction of knowledge from data is a relatively recent computational pursuit which has been the focus of significant research attention and has an extensive field of potential applications. With the advent of widespread data collection describing a variety of systems spanning many fields of...
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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...