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Fall 2018

Xu, Peng

Information extraction (IE) is one of the most important technologies in the information age. Applying information extraction to text is linked to the prob- lem of text simplification in order to create a structured view of the informa- tion present in free text. However, information extraction...

Fall 2023

Doosti Sanjani, Anahita

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...

Fall 2019

Surma,Christopher

This thesis aims to develop a motion control strategy for an Unmanned Aerial Vehicle (UAV) to execute a pursuit algorithm based on a vision based object detection algorithm. This enables a pursuer UAV to follow a target UAV based on images obtained from the onboard camera of the pursuer. The UAV...

Fall 2020

Ashley, Dylan R

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...

Fall 2022

Sayehban, Shahrzad

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...

Spring 2020

Behboudian, Paniz

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...

Fall 2023

Alam Anik, Md Tanvir

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...

Spring 2021

Wen, Junfeng

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...

Spring 2021

Southcott, Jordan

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...

Fall 2019

Jacobsen, Andrew

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,...