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Filtering by: Creator Biever, K. D. Remove constraint Creator: Biever, K. D. Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

    Creator:
    Senewiratne, Yomal K.
    Abstract:
    We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
    Resource Type:
    Masters Thesis
    Full Text:
    map, we create additional training data ωj at each trial j using the current dataset D as follows: we

  • 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
    Full Text:
    Boosting . . . . . . . . . . . . . . . . . . . . 43 4.3.3 K-shot Setting

  • Navigation and coordination of autonomous mobile robots with limited resources

    Creator:
    Knudson, Matthew D.
    Abstract:
    The use of autonomous robots in complex exploration tasks is rapidly increasing. Indeed, robots can provide speed and cost effectiveness in many tasks, as well as allow operation in environments that are hostile to humans. In this dissertation we: 1) provide two adaptive navigation algorithms; 2) develop a coordination mechanism;...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Matthew D. Knudson for

  • Applying hierarchical and adaptive control to coordinating simple robots

    Creator:
    Knudson, Matthew D.
    Abstract:
    Coordinating multiple robots to achieve a complex task requires solving two distinct control problems: the high-level control problem of ensuring that each robot aims to perform a useful task (e.g., coordination) and the low-level control problem of ensuring that each robot actually performs the correct actions to achieve its task...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Matthew D. Knudson for the

  • 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:
    /��+/�@Y��/�A�1�7�<"�Z3[&2@0�!/K< W &2 \<"�X&27�7�#]�.M�D"�9 \'E&2M�7��?$.-0H]&E�+/�D=1GD"�+/G@]�)I^&2 "D

  • 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:
    select k ≥ 1 points to be evaluated. The results of those points are then added to the set of

  • Statistical Properties and Guarantees for Certain Anomaly Detection Relevant Problems

    Creator:
    Liu, Si
    Abstract:
    Anomaly detection aims at detecting the points that appear different than the majority of the data, such that they are suspected to be generated from a different distribution. Anomaly detectors have been applied in many different fields, such as detecting fraudulent behaviors in bank transaction, finding broken sensors in a...
    Resource Type:
    Dissertation
    Full Text:
    points in S, each has confidence level 1� �m|S| 9: Compute d(k)lower, the k th smallest lower bound from

  • Protein secondary structure prediction using conditional random fields and profiles

    Creator:
    Shen, Rongkun
    Abstract:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
    Resource Type:
    Masters Thesis
    Full Text:
    test on the development set D Let kθ be the best parameter setting found Train

  • 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:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Empirical P[K > k] vs. D . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3

  • Weakly Supervised Learning for Activity Recognition from Time Series Data

    Creator:
    Guan, Xinze
    Abstract:
    The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
    Resource Type:
    Dissertation
    Full Text:
    Step Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 D Maximization Step Details