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Filtering by: Creator Hollinger, Geoffrey A. Remove constraint Creator: Hollinger, Geoffrey A. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Multiagent learning for locomotion and coordination in tensegrity robotics

    Creator:
    Iscen, Atil
    Abstract:
    Tensegrity structures are composed of pure compressional elements that are connected via a network of pure tensional elements. The concept of tensegrity promises numerous advantages to the field of robotics. Tensegrity robots are, however, notoriously difficult to control due to their oscillatory nature and nonlinear interaction between the components. Multiagent...
    Resource Type:
    Dissertation
    Full Text:
    : Kagan Tumer Geoffrey Hollinger Tensegrity structures are composed of pure compressional elements that

  • High-Dimensional Reinforcement Learning with Human Feedback

    Creator:
    Curran, William
    Abstract:
    State-of-the-art personal robots need to perform complex manipulation tasks to be viable in complex scenarios. However, many of these robots, like the PR2, use manipulators with high degrees of freedom. High degrees of freedom are desirable from a functionality standpoint, but make the learning task more difficult by adding a...
    Resource Type:
    Dissertation
    Full Text:
    degrees of freedom are desirable from a functionality standpoint, but make the learning task more

  • Individual Fairness Optimization in Machine Learning with Minimax Loss and an Abstain Option

    Creator:
    Behnoudfar, Ali
    Abstract:
    In this thesis, a new learning algorithm is introduced that is targeted towards individual fairness. In order to be individually fair, mispredictions need to be avoided as each such prediction means the learning algorithm was unfair towards some individual. Therefore, achieving individual fairness implies having a perfect classifier, which is...
    Resource Type:
    Masters Thesis
    Full Text:
    approved: Fuxin Li In this thesis, a new learning algorithm is introduced that is targeted towards

  • Methods for Detection and Recovery of Out-of-Distribution Examples

    Creator:
    Ratzlaff, Neale J.
    Abstract:
    Deep neural networks currently comprise the backbone of many applications where safety is a critical concern, for example: autonomous driving and medical diagnostics. Unfortunately these systems currently fail to detect out-of-distribution (OOD) inputs and can be prone to making dangerous errors when exposed to them. In addition, these same systems...
    Resource Type:
    Masters Thesis
    Full Text:
    currently comprise the backbone of many applications where safety is a critical concern, for example

  • Uncertainty in Deep Learning with Implicit Neural Networks

    Creator:
    Ratzlaff, Neale
    Abstract:
    The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
    Resource Type:
    Dissertation
    Full Text:
    , Geoffrey Hollinger, and Stefano Ermon. Tom and Alan in particu- lar have provided consistent, invaluable

  • Visual object discovery and understanding

    Creator:
    Yuan, Jialin
    Abstract:
    Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
    Resource Type:
    Dissertation
    Full Text:
    a fundamental and essential step in human perception and understanding of the world. Accordingly

  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
    Resource Type:
    Dissertation
    Full Text:
    Ramsey We describe a series of novel computational models, CERENKOV (Computational Elu- cidation of the

  • A Distributed, Component-based Solution for Scientific Information Management

    Creator:
    Jain, Lalit Kumar
    Abstract:
    This paper describes the design and performance of a distributed, multi-tier architecture for scientific information management and data exploration. A novel aspect of this framework is its integration of Java IDL, the CORBA distributed object computing middleware with JavaBeans, the Java Component model to provide a flexible, interactive framework for...
    Resource Type:
    Capstone Project
    Full Text:
    A Distributed, Component-based Solution for Scientific Information

  • Computer performance and evaluation utilizating the resource planning and management system

    Creator:
    Elliott, Jimmie Lynn
    Abstract:
    A review of current computer performance and evaluation techniques reveals a lack of an acceptable analytic tool for optimal computer system performance and evaluation. A generalized approach to the formulation of a third generation computer system model is proposed. The approach is used to optimize computer resource utilization and to...
    Resource Type:
    Masters Thesis
    Full Text:
    privacy Robert A. Short A review of current computer performance and evaluation tech- niques reveals a

  • Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection

    Creator:
    Seo, Eugene
    Abstract:
    Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
    Resource Type:
    Dissertation
    Full Text:
    : Rebecca A. Hutchinson Ecological domains seeking to understand the environment and the behavior of