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Filtering by: Creator Pham, Long V. Remove constraint Creator: Pham, Long V. Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Data-Driven Approaches For Decoding Volitional Movement Intent From Bioelectrical Signals

    Creator:
    Cunha Dantas, Henrique
    Abstract:
    There are nearly two million limb amputees living in the United States of America. Loss of limbs results in profound changes in one's life. However, the underlying neural circuitry and much of the ability to sense and control movements of their missing limb is retained even after limb loss. This...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: V John Mathews There are nearly two million limb amputees living in the United States

  • Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

    Creator:
    George, Damon A.
    Abstract:
    Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
    Resource Type:
    Masters Thesis
    Full Text:
    Decoding for Intuitive Control of Neuroprostheses Abstract approved: V John Mathews In the United States

  • Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

    Creator:
    Khorram, Saeed
    Abstract:
    The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
    Resource Type:
    Dissertation
    Full Text:
    : Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
    Resource Type:
    Dissertation
    Full Text:
    fast training speed. However, for problems where there exist long range dependencies between the

  • Robust and Efficient Classification of Videos in the Wild

    Creator:
    Mahasseni, Behrooz
    Abstract:
    Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
    Resource Type:
    Dissertation
    Full Text:
    actions in videos is a long-standing problem in computer vision with a wide range of applications

  • Adaptation-based programming

    Creator:
    Bauer, Tim
    Abstract:
    Partial programming is a field of study where users specify an outline or skeleton of a program, but leave various parts undefined. The undefined parts are then completed by an external mechanism to form a complete program. Adaptation-Based Programming (ABP) is a method of partial programming that utilizes techniques from...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.1.1 HAM— Hierarchies of Abstract Machines . . . . . . . . . . . . . . . 28 3.1.2 PHAM

  • Effective decision-theoretic assistance through relational hierarchical models

    Creator:
    Natarajan, Sriraam
    Abstract:
    Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...
    Resource Type:
    Dissertation
    Full Text:
    approved: Prasad Tadepalli Building intelligent computer assistants has been a long-cherished goal of AI

  • Efficient Incremental Panorama Reconstruction from Multiple Videos

    Creator:
    Feng, Zhongyuan
    Abstract:
    Constructing a panorama from a set of videos is a long-standing problem in computer vision. A panorama represents an enhanced still-image representation of an entire scene captured in a set of videos, where each video shows only a part of the scene. Importantly, a panorama shows only the scene background,...
    Resource Type:
    Capstone Project
    Full Text:
    Constructing a panorama from a set of videos is a long-standing problem in computer vision. A panorama

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
    Full Text:
    biological contexts have limited types in long sequences of tokens. A sentence has a possible vocabulary

  • Weakly Supervised Learning for Activity Recognition from Time Series Data

    Creator:
    Guan, Xinze
    Abstract:
    The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
    Resource Type:
    Dissertation
    Full Text:
    and low cost, have provided a viable and non-intrusive approach for long-term activ- ity monitoring