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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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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 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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Utilizing Positron Emission Tomography to Detect Functional Changes Following Drug Therapy in a Renal Cell Carcinoma Mouse Model
DownloadSpring 2013
Sunitinib is currently the first line drug therapy for metastasizing renal cell carcinoma (RCC). It has been shown to have a profound effect on tumor angiogenesis leading to modifications of the tumor’s microenvironment. Tumor hypoxia plays an important role in the metastatic potential of a solid...
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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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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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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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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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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...