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Filtering by: Creator Henderson, E. P. (Eric P.) Remove constraint Creator: Henderson, E. P. (Eric P.) Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Theory and Implementation of a Variational Database Management System

    Creator:
    Ataei, Parisa S.
    Abstract:
    In this thesis, I present the variational database management system, a formal framework and its implementation for representing variation in relational databases and managing variational information needs. A variational database is intended to support any kind of variation in a database. Specific kinds of variation in databases have already been...
    Resource Type:
    Dissertation
    Full Text:
    Implementation of a Variational Database Management System Abstract approved: Eric Walkingshaw In this thesis

  • Monte Carlo Counterfactual Regret Minimization applied to Clue

    Creator:
    Martin, Benjamin
    Abstract:
    This document analyzes the application of Monte Carlo Counterfactual Regret Minimization (MCCFR) in the game of Hasboro’s Clue. As a partially observable stochastic multiplayer game, Clue is well-suited for MCCFR methods. MCCFR has previously been shown to be effective in beating top human players around the world in No-Limit Texas...
    Resource Type:
    Honors College Thesis
    Full Text:
    Words: clue, cluedo, MCCFR, monte carlo, partially observable games Corresponding e-mail address

  • Behavior graphs for data-driven animation of 3d characters

    Creator:
    Srinivasan, Madhusudhanan
    Abstract:
    In this dissertation, we present a user-in-the-loop method for the design of an interactive motion data structure that benefits from the advantages of both motion graphs and blend-based techniques. Our novel approach automatically analyzes a traditional motion graph built from labeled motion clips. The result is a more condensed, coarser...
    Resource Type:
    Dissertation
    Full Text:
    faculty of the nVidia Graphics Lab, School of EECS - Dr. Eric Mortensen, Dr. Mike Bailey, and Dr. Eugene

  • 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:
    D,E, and F. There is not any muscle connecting D to F. There are two possibilities for the next

  • Image features and learning algorithms for biological, generic and social object recognition

    Creator:
    Zhang, Wei
    Abstract:
    Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require...
    Resource Type:
    Dissertation
    Full Text:
    guidance have provided the best basis for this dissertation. I am pleased to thank Professor Eric

  • Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

    Creator:
    Raghavan, Aswin
    Abstract:
    Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
    Resource Type:
    Dissertation
    Full Text:
    pruning operator P(D,π) ≡ Pπ(D) for any ADD D and a policy constraint BDD π

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
    Full Text:
    predicted for the message e 8: predicted_tags← NoIf_Predict(e, θ, nf) 9: for each (t, p) in predicted_tags

  • Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

    Creator:
    Nguyen, Khoi D.
    Abstract:
    This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
    Resource Type:
    Dissertation

  • 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:
    -dependent Poisson distribution: p(l|c) = λ l c l! e−λc , (3.3) where λlc is estimated action mean

  • 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:
    ≥ P̄ t−1 k + 1 + λ. Algorithm 1 summarizes the key-frame selection process. For other parameters, e is