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Fall 2022

Richard, Daniel

Universal approximation refers to the property of a collection of functions to approximate continuous functions. Past literature has demonstrated that neural networks are dense in continuous functions on compact subsets of finite-dimensional spaces, and this document extends those findings to...

Fall 2023

Das, Debarpan

Skin moles are one of the most commonly occurring dermatological conditions prevalent nowadays. Early identification and diagnosis of moles are absolutely crucial since they often turn out to be precursors to serious conditions such as melanoma, a dangerous type of skin cancer. Therefore, to...

Spring 2020

Mohammed Omar Haridy Kayed

In this research, a developed n-type piezoresistive three dimensional (3D) stress sensor with full temperature compensation is presented. The proposed sensing rosette benefits from the stress insensitivity of the full-circular n-type piezoresistor, oriented over (111) silicon plane, to detect...

Fall 2023

Awwal, Alvina

The problem of missing data is omnipresent in a wide range of real-world datasets. When learning and predicting on this data with neural networks, the typical strategy is to fill-in or complete these missing values in the dataset, called impute-then-regress. Much less common is to attempt to...

Spring 2019

Imani, Ehsan

In this thesis we introduce a new loss for regression, the Histogram Loss. There is some evidence that, in the problem of sequential decision making, estimating the full distribution of return offers a considerable gain in performance, even though only the mean of that distribution is used in...

Fall 2022

Johnson, Neil Wallace

Dynamic tumour-tracked radiotherapy is a promising method for delivering conformal doses to tumours that exhibit a large degree of motion as a result of patient respiration. However, there exists an inevitable latency between the acquisition of an image of a moving tumour and the adaptation of...

Fall 2021

Al Dallal, Ahmed

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.,...

Fall 2022

Li, Tianhua

Machine learning has been used to solve single-agent search problems. One of its applications is to guide search algorithms by learning heuristics. However, it is difficult to provide guarantees on the quality of learning from a neural network, since the resulting heuristics can be inadmissible,...

Fall 2023

Alateeq, Majed Mohammad

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...

Spring 2020

Samson, Matthew

Mineral resource estimation is an integral part of making informed decisions while evaluating the feasibility of a mining operation. Geostatistical tools estimate geological and spatial features with the assumptions of first and second‑order stationarity. In the modelling process,...