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Filtering by: Creator Guyette, Richard P. Remove constraint Creator: Guyette, Richard P. Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Toward Automated Decision-Making in Power Systems Wide-Area Protection

    Creator:
    Meier, Richard John
    Abstract:
    In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements and despite targeted efforts to improve the operation and control of the electric grid, the power systems industry has seen relatively small advances in this regard. For instance, today's power system is...
    Resource Type:
    Masters Thesis
    Full Text:
    Wide-Area Protection AN ABSTRACT OF THE THESIS OF Richard John Meier for the degree of Master of

  • Planning in multi-agent partially observable domains using sparse sampling

    Creator:
    Donneau-Golencer, Thierry D.
    Abstract:
    A large number of sequential decision-making problems in uncertain environments can be modeled as Markov Decision Processes (MDPs). In such settings, an agent can observe at each time step the state of the environment and then executes an action, causing a stochastic transition to a new state of the environment...
    Resource Type:
    Masters Thesis
    Full Text:
    \<>�9I_<"��� D\<p&r<>� -2:s<"��� �!/K')�+ \-0/�HJ�!/K���9/_�quG�q3 8!1�<>�!D &E/v&289<"�+-0/sQ

  • Object detection in biological images using a search-based framework

    Creator:
    Lam, Michael
    Abstract:
    This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
    Resource Type:
    Masters Thesis
    Full Text:
    a pruning function (P) to remove bad candidate labelings generated by the successor function. This

  • Incorporating labeled features into image classification using generalized expectation

    Creator:
    Moore, Travis
    Abstract:
    Image classification is a difficult problem, often requiring large training sets to get satisfactory results. However this is a task that humans perform very well, and incorporating user feedback into these learning algorithms could help reduce the dependency on large amounts of labeled training data. This process has already been...
    Resource Type:
    Masters Thesis
    Full Text:
    models the conditional distribution P (~Y | ~X) as a product of factors, where each factor is the

  • Action Segmentation with Limited Supervision

    Creator:
    Li, Jun
    Abstract:
    In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2 Computing the logadd score of all paths in P , for the discriminative forward loss LDF

  • Probabilities of voting paradoxes with three candidates

    Creator:
    McDonald, Leslie M.
    Abstract:
    Pardoxes in voting has been an interest of voting theorists since the 1800's when Condorcet demonstrated the key example of a voting paradox: voters with individually transitive rankings produce an election outcome which is not transitive. With Arrow's Impossibility Theorem, the hope of finding a fair voting method which accurately...
    Resource Type:
    Masters Thesis
    Full Text:
    1970’s. Such people as Richard Niemi, Peter Fishburn, and William Gehrlein utilized probability

  • 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:
    for panorama P initialization, measured in minutes

  • Structured learning with latent variables : theory and algorithms

    Creator:
    Zhao, Kai
    Abstract:
    Most tasks in natural language processing (NLP) try to map structured input (e.g., sentence or word sequence) to some form of structured output (tag sequence, parse tree, semantic graph, translated/paraphrased/compressed sentence), a problem known as “structured prediction”. While various learning algorithms such as the perceptron, maximum entropy, and expectation-maximization have...
    Resource Type:
    Dissertation

  • Network Coding, Random Matrices, and Their Applications to Communication Systems

    Creator:
    Nguyen-Huu, Duong
    Abstract:
    In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
    Resource Type:
    Dissertation
    Full Text:
    : Thinh P. Nguyen In this work, we study network coding technique, its relation to random matrices, and

  • Parsing with Recurrent Neural Networks

    Creator:
    Cross, James Henry, III
    Abstract:
    Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...
    Resource Type:
    Dissertation
    Full Text:
    representation: p = tanh(W [x1;x2] + b) (2.3) Note that at the leaf level, the inputs to the network are vector