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Skip to Search Results- 144Machine Learning
- 19Artificial Intelligence
- 17Reinforcement Learning
- 15Deep Learning
- 10Computer Vision
- 10Natural Language Processing
- 2Wen, Junfeng
- 1Aghaei, Nikoo
- 1Al Dallal, Ahmed
- 1Al-Masri, Mohammad
- 1Alam Anik, Md Tanvir
- 1Alateeq, Majed Mohammad
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Wildfire Fuel Mapping with Convolutional Neural Networks for Remote Automated Exposure Assessment
DownloadFall 2023
While beneficial to the natural environment in many cases, wildfires become hazardous when they intersect with the built environment. As such, there is an ongoing effort to understand the fire environment, the fuels it contains, and the way that wildfire interacts with the built environment. In...
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Spring 2023
Many competitive online video games release new characters on a regular basis. Designing these characters requires significant effort on several aspects including art, story, music, and game balance. Thus automating the design of these aspects offers value in saving human effort. This thesis...
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Spring 2021
This thesis evaluated Convoultional LSTM (ConvLSTM) for frame prediction to help better understand motion in neural networks. Three different neural networks were implemented and trained. The three networks included, the original ConvLSTM paper by Shi et al. [35], the Spatio-Temporal network by...
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Fall 2019
In this thesis, we investigate different vector step-size adaptation approaches for continual, online prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad,...
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Validation and Pattern Discovery in the Canadian Community Health Survey - Mental Health (CCHS-MH) Support Utilization
DownloadSpring 2021
Mental illness is one of the most pressing medical challenges facing society. Although identifying gaps in mental-health support utilization is important for public health, this topic has not been widely explored in the literature. The latest Canadian Community Health Survey - Mental Health...
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Spring 2021
This dissertation demonstrates how to utilize data collected previously from different sources to facilitate learning and inference for a target task. Learning from scratch for a target task or environment can be expensive and time-consuming. To address this problem, we make three contributions...
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Fall 2023
Giving reasons for justifying the decisions made by classification models has received less attention in recent artificial intelligence breakthroughs than improving the accuracy of the models. Recently, AI researchers are paying more attention to filling this gap, leading to the introduction of...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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Fall 2022
Sentence reconstruction and generation are essential applications in Natural Language Processing (NLP). Early studies were based on classic methods such as production rules and statistical models. Recently, the prevailing models typically use deep neural networks. In this study, we utilize deep...
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Fall 2020
This thesis is offered as a step forward in our understanding of forgetting in artificial neural networks. ANNs are a learning system loosely based on our understanding of the brain and are responsible for recent breakthroughs in artificial intelligence. However, they have been reported to be...