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  • Error Analysis of Object Detection Models with Interactive User Interfaces

    Creator:
    Oh, Dayeon
    Abstract:
    Object detection models are being widely used in many applications, such as autonomous driving, construction management, and cancer detection. Evaluating the performance of the object detection model is more complicated than other computer vision models such as image classification models. Most of the images have several objects to be detected,...
    Resource Type:
    Capstone Project

  • An Agent-based Dynamic Network Model

    Creator:
    Yu, Jingtian
    Abstract:
    Agent-based models (ABM) are widely used in network data analysis, and due to their simple structures and sophisticated outcomes, they serve as good tools in understanding the dynamics in networks. In this thesis, we develop an agent-based dynamic network model, and show that it can replicate the expected degree distribution...
    Resource Type:
    Masters Thesis

  • Embedded Reachability for Autonomous Racecar

    Creator:
    Jewell, Nathaniel C.
    Abstract:
    Reachability analysis enables the safety assurance of control systems despite uncertain initial conditions and control inputs, and can be an important component to run on-board an autonomous system. This thesis explores the characteristics of reachability analysis with different algorithmic configurations and runtime parameters running onboard the F1Tenth 1/10th-scale autonomous racing...
    Resource Type:
    Honors College Thesis
    Full Text:
    _____________________________________________________________________ Weng-Keen Wong, Committee Member, representing EECS

  • Active Learning from Examples, Queries and Explanations

    Creator:
    Hamidi-Haines, Mandana
    Abstract:
    Humans are remarkably efficient in learning by interacting with other people and observing their behavior. Children learn by watching their parents’ actions and mimic their behavior. When they are not sure about their parents demonstration, they communicate with them, ask questions, and learn from their feedback. On the other hand,...
    Resource Type:
    Dissertation
    Full Text:
    committee members, Professor Alan Fern and Professor Weng-Keen Wong for the innumerable discussions about

  • Open Set Learning with Counterfactual Images

    Creator:
    Neal, Lawrence W.
    Abstract:
    In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
    Resource Type:
    Masters Thesis
    Full Text:
    -Keen Wong were critical to the success of this project. I would further like to acknowledge my

  • Classifier Chains for Multi-Label Classification with Incomplete Labels

    Creator:
    Almuallim, Jafer
    Abstract:
    Many methods have been explored in the literature of multi-label learning, ranging from simple problem transformation to more complex method that capture correlation among labels. However, mostly all existing works do not address the challenge with incomplete label data. The goal of this project is to extend the work of...
    Resource Type:
    Capstone Project

  • Active learning of constraints for semi-supervised clustering

    Creator:
    Xiong, Sicheng
    Abstract:
    Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints. In this paper, we study the active learning problem of selecting pairwise must-link and cannot-link constraints for semisupervised clustering. We consider active learning in an iterative manner where in each iteration queries are...
    Resource Type:
    Masters Thesis
    Full Text:
    committee: Prasad Tadepalli, Weng-Keen Wong, and Bernhard Jenny. I would like to acknowledge the support of

  • Automatic Explanations of Computation Results with Value Decompositions and Dominating Sets

    Creator:
    Kumar, Prashant
    Abstract:
    In an increasingly computation-driven world, algorithms and mathematical models significantly impact decision making across various fields. To foster trust and understanding, it is crucial to provide users with clear and concise explanations of the reasoning behind the results produced by computational tools, especially when recommendations appear counterintuitive. Legal frameworks in...
    Resource Type:
    Dissertation
    Full Text:
    my committee members Dr. Alan Fern, Dr. Weng-Keen Wong, Dr. Mike Bailey, and Dr. Yelda Turkan for

  • Volume minimized generalized cross entropy for learning with label noise

    Creator:
    Wolnick, Daniel
    Abstract:
    The types and rates of label noise in real-world data sets present a challenge to machine learning projects. In this thesis, we propose a novel approach to address this issue. Our method combines a noise modelling technique for correcting label noise across the entire data set with a robust loss...
    Resource Type:
    Honors College Thesis
    Full Text:
    . APPROVED: Xiao Fu, Mentor, representing School of Electrical Engineering and Computer Science Weng-Keen

  • Investigating Latent State and Uncertainty Representations in Reinforcement Learning

    Creator:
    Koul, Anurag
    Abstract:
    Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
    Resource Type:
    Dissertation
    Full Text:
    show my gratitude to my committee members: Prof. Prasad Tadepalli, Prof. Fuxin Li, and Prof. Weng-Keng