Now showing items 1-20 of 68

    • AdvEx: Interactive Visual Explorations of Adversarial Evasion Attacks 

      You, Yuzhe (University of Waterloo, 2023-06-28)
      Adversarial machine learning (AML) focuses on studying attacks that can fool machine learning algorithms into generating incorrect outcomes as well as the defenses against worst-case attacks to strengthen the adversarial ...
    • Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge 

      Fenaux, Lucas (University of Waterloo, 2024-01-22)
      Adversarial examples are malicious inputs to trained machine learning models supplied to trigger a misclassification. This type of attack has been studied for close to a decade, and we find that there is a lack of study ...
    • Analyzing Threats of Large-Scale Machine Learning Systems 

      Lukas, Nils (University of Waterloo, 2024-02-22)
      Large-scale machine learning systems such as ChatGPT rapidly transform how we interact with and trust digital media. However, the emergence of such a powerful technology faces a dual-use dilemma. While it can have many ...
    • An Application of Out-of-Distribution Detection for Two-Stage Object Detection Networks 

      Denouden, Taylor (University of Waterloo, 2020-02-14)
      Recently, much research has been published for detecting when a classification neural network is presented with data that does not fit into one of the class labels the network learned at train time. These so-called ...
    • Applications of Deep Learning to Differential Equation Models in Oncology 

      Meaney, Cameron (University of Waterloo, 2023-07-25)
      The integration of quantitative tools in biology and medicine has led to many groundbreaking advances in recent history, with many more promising discoveries on the horizon. Conventional mathematical models, particularly ...
    • Assessing the Trainability of the Variational Quantum State Diagonalization Algorithm at Scale 

      Arrow, Joan (University of Waterloo, 2022-04-28)
      Quantum algorithm development is a famously difficult problem. The lack of intuition concerning the quantum realm makes constructing quantum algorithms which solve partic- ular problems of interest difficult. In addition, ...
    • Automated Knowledge Discovery using Neural Networks 

      Panju, Maysum (University of Waterloo, 2021-05-21)
      The natural world is known to consistently abide by scientific laws that can be expressed concisely in mathematical terms, including differential equations. To understand the patterns that define these scientific laws, it ...
    • Automating Programming Assignment Marking with AST Analysis 

      Li, Sichuang (University of Waterloo, 2019-01-14)
      This thesis presents a novel approach to automatically mark programming assignments. We hypothesize that correct student solution ASTs will be more similar to reference solution ASTs than incorrect student solutions and ...
    • Bayesian Federated Learning in Predictive Space 

      Hasan, Mohsin (University of Waterloo, 2023-08-10)
      Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client's data is private. This paradigm is useful in settings where different entities own different ...
    • BotChase: Graph-Based Bot Detection Using Machine Learning 

      Abou Daya, Abbas (University of Waterloo, 2019-05-21)
      Bot detection using machine learning (ML), with network flow-level features, has been extensively studied in the literature. However, existing flow-based approaches typically incur a high computational overhead and do not ...
    • Case Studies of a Machine Learning Process for Improving the Accuracy of Static Analysis Tools 

      Zhao, Peng (University of Waterloo, 2016-10-18)
      Static analysis tools analyze source code and report suspected problems as warnings to the user. The use of these tools is a key feature of most modern software development processes; however, the tools tend to generate ...
    • The Computational Advantages of Intrinsic Plasticity in Neural Networks 

      Shaw, Nolan (University of Waterloo, 2019-10-17)
      In this work, I study the relationship between a local, intrinsic update mechanism and a synaptic, error-based learning mechanism in ANNs. I present a local intrinsic rule that I developed, dubbed IP, that was inspired by ...
    • Contributions to Unsupervised and Semi-Supervised Learning 

      Pal, David (University of Waterloo, 2009-05-22)
      This thesis studies two problems in theoretical machine learning. The first part of the thesis investigates the statistical stability of clustering algorithms. In the second part, we study the relative advantage of ...
    • Controlled Generation of Stylized Text Using Semantic and Phonetic Representations 

      Gudmundsson, Egill Ian (University of Waterloo, 2022-01-21)
      Neural networks are a popular choice of models for the purpose of text generation. Variational autoencoders have been shown to be good at reconstructing text and generating novel text. However, controlling certain aspects ...
    • Critical Dynamics In Population Vaccinating Behavior 

      Pananos, A. Demetri; Bury, Thomas M.; Wang, Clara; Schonfeld, Justin; Mohanty, Sharada P.; Nyhan, Brendan; Salathe, Marcel; Bauch, Chris T. (National Academy of Sciences, 2017-12-26)
      Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics ...
    • Designing a Unity Plugin to Predict Expected Affect in Games Using Biophilia 

      Zhang, Licheng (University of Waterloo, 2022-09-28)
      Video games can generate different emotional states and affective reactions, but it can sometimes be difficult for a game’s visual designer to predict the emotional response a player might experience when designing a game ...
    • Designing Intelligent Systems to Support Workspace Collaboration 

      Jahangirzadeh Soure, Ehsan (University of Waterloo, 2023-02-06)
      Complex problems and interprofessional work require more resources to be involved, which has been possible through collaboration. Collaborative work is evolving from physical collaboration to more virtual forms through ...
    • Disentanglement of Syntactic Components for Text Generation 

      Das, Utsav Tushar (University of Waterloo, 2022-02-18)
      Modelling human generated text, i.e., natural language data, is an important challenge in artificial intelligence. A good AI program should be able to understand and analyze natural language, and generate fluent and accurate ...
    • Distributions in Semantic Space 

      Selby, Kira (University of Waterloo, 2024-04-26)
      This thesis is an investigation of the powerful and flexible applications of analyzing empirical distributions of vectors within latent spaces. These methods have historically been applied with great success to the domain ...
    • Effects of Developmental Heuristics for Natural Language Learning 

      Engels, Steve (University of Waterloo, 2003)
      Machine learning in natural language has been a widely pursued area of research. However, few learning techniques model themselves after human learning, despite the nature of the task being closely connected to human ...


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