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Filtering by: Creator Impara, Peter C. Remove constraint Creator: Impara, Peter C. Committee Member Wong, Weng-Keen Remove constraint Committee Member: Wong, Weng-Keen

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  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
    Resource Type:
    Dissertation
    Full Text:
    applicable more broadly to other areas of data science. c�Copyright by Padideh Danaee December 6, 2019

  • Network Analysis and Change-point Detection

    Creator:
    Xu, Jiarui
    Abstract:
    Analysis of observations on sequential events over time is common in real life. Sequential measurements over time describing the behavior of systems are usually called time series data, which have been collected in a wide range of disciplines. Over the years there have been multiple research areas in studying stochastic...
    Resource Type:
    Dissertation
    Full Text:
    detection of time points where speci�c characteristics of the time series change. Network data sets

  • 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:
    accuracy. c©Copyright by Junyuan Lin February 18, 2013 All Rights Reserved A Study of Methods for

  • Network Traffic-Driven Hybrid Learning for Classifying Seen and Unseen IoT Device Types

    Creator:
    Bao, Jiaqi
    Abstract:
    IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks,...
    Resource Type:
    Masters Thesis
    Full Text:
    overhead. c©Copyright by Jiaqi Bao March 17, 2020 All Rights Reserved Network Traffic-Driven Hybrid

  • Modeling protein structure at atomic resolution

    Creator:
    Berkholz, Donald S.
    Abstract:
    This thesis includes three studies involving different aspects of modeling protein structure. The first study illustrates the levels of insight available from atomic-resolution protein structures. The second study derives general trends of protein geometry from atomic-resolution structures and shows their implications for modeling. The third study creates a model of...
    Resource Type:
    Dissertation
    Full Text:
    , except for the final rounds. Peter Krenesky designed the database backend for Chapter 3 and made its

  • A SAT-based planning framework for optimizing resource production

    Creator:
    Ventura, Christopher
    Abstract:
    Domain-independent automated planning is concerned with computing a sequence of actions that can transform an initial state into a desired goal state. Resource production domains form an interesting class of such problems, in that they typically require reasoning about concurrent durative-actions with continuous effects while minimizing some cost function. Although...
    Resource Type:
    Masters Thesis
    Full Text:
    C:/Documents and Settings/Chris Ventura/Desktop/thesis/thesis.dvi

  • Investigating Latent State and Uncertainty Representations in Reinforcement Learning

    Creator:
    Koul, Anurag
    Abstract:
    Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
    Resource Type:
    Dissertation
    Full Text:
    transition for a depth d using mode action of current stochastic policy. c) MCTS iteratively extends a

  • Re-understanding Finite-State Representations of Recurrent Policy Networks

    Creator:
    Danesh, Mohamad H.
    Abstract:
    We propose an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minimization process can obscure a deeper understanding...
    Resource Type:
    Masters Thesis
    Full Text:
    finite states and observations. c) Interpretable reductions (Section 3.3.1) are applied to the MM

  • 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:
    typical feedforward neural network representation or two hidden layer and one output layer. (c) A

  • 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:
    eval- uated in the environment (c), evolved with CCEA, and the top performing agents are selected for