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Filtering by: Creator Desiderio, Russell A. Remove constraint Creator: Desiderio, Russell A. Committee Member Wong, Weng-Keen Remove constraint Committee Member: Wong, Weng-Keen

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  • Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

    Creator:
    Gómez Fernández, Mario Enrique
    Abstract:
    Known as the fourth industrial revolution, digitization is an ongoing trend in all fields, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and...
    Resource Type:
    Dissertation
    Full Text:
    Engineering Abstract approved: Kathryn A. Higley Known as the fourth industrial revolution, digitization is

  • Learning to coordinate in sparse asymmetric multiagent systems

    Creator:
    Dixit, Gaurav
    Abstract:
    Multiagent learning offers a rich framework to address challenging real-world problems such as remote exploration and healthcare coordination, which require autonomous agents to express elaborate interactions. To be effective in such systems, agents must collectively reason about and pursue high-level, long-term, and possibly nebulous objectives while adapting their strategy to...
    Resource Type:
    Dissertation
    Full Text:
    a rich framework to address challenging real-world prob- lems such as remote exploration and

  • A study of methods for fine-grained object classification of arthropod specimens

    Creator:
    Lin, Junyuan
    Abstract:
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
    Resource Type:
    Masters Thesis
    Full Text:
    Master of Science in Computer Science presented on February 18, 2013. Title: A Study of Methods for

  • Evolving character controllers for collision preparation

    Creator:
    Rose, Robert W.
    Abstract:
    Full dynamic control of physically simulated characters is the holy grail of video games and other interactive applications. Recent advances in the field have presented controllers that can handle the balance and fall response after a collision. This thesis addresses the development of a controller that handles the response before...
    Resource Type:
    Masters Thesis
    Full Text:
    : Evolving Character Controllers for Collision Preparation Abstract approved: Ronald A. Metoyer Full

  • Bayesian optimization with empirical constraints

    Creator:
    Azimi, Javad
    Abstract:
    Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
    Resource Type:
    Dissertation
    Full Text:
    typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms

  • Hierarchical structure discovery and transfer in sequential decision problems

    Creator:
    Mehta, Neville
    Abstract:
    Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal. The first...
    Resource Type:
    Dissertation
    Full Text:
    . The MASH framework facilitates transfer among multiple agents coordinating within a domain. The VRHRL

  • Toward end-user debugging of machine-learned programs

    Creator:
    Kulesza, Todd
    Abstract:
    The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters because without the ability to fix errors, users may find that the learned program’s errors...
    Resource Type:
    Masters Thesis
    Full Text:
    results of a machine learning from user behavior can be thought of as a program, and like all programs

  • Probabilistic models for quality control in environmental sensor networks

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
    Resource Type:
    Dissertation
    Full Text:
    . Dietterich Networks of distributed, remote sensors are providing ecological scientists with a view of our

  • New Directions in Search-based Structured Prediction: Multi-Task Learning and Integration of Deep Models

    Creator:
    Ma, Chao
    Abstract:
    This thesis studies the problem of structured prediction (SP), where the agent needs to predict a structured output for a given structured input (e.g., Part-of-Speech tagging sequence for an input sentence). Many important applications including machine translation in natural language processing (NLP) and image interpretation in computer vision can be...
    Resource Type:
    Dissertation
    Full Text:
    ), where the agent needs to predict a structured output for a given structured input (e.g., Part-of-Speech

  • 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