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Filtering by: Creator Greer, Adam T. Remove constraint Creator: Greer, Adam T. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Peer Reviewed No Remove constraint Peer Reviewed: No

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  • Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

    Creator:
    Marcon dos Santos, Gilberto
    Abstract:
    This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost...
    Resource Type:
    Dissertation

  • Towards reliable and survivable ocean wave energy converters

    Creator:
    Brown, Adam C.
    Abstract:
    Ocean wave energy is a new and developing field of renewable energy with great potential. The energy contained in one meter of an average wave off the coast of Newport Oregon could supply dozens of homes with electricity. However, ocean waves are usually quite irregular which leads to large bursts...
    Resource Type:
    Masters Thesis
    Full Text:
    OF THE THESIS OF Adam C Brown for the degree of Master of Science in Mechanical Engineering

  • A numerical approach to Tamme's problem in euclidean n-space

    Creator:
    Adams, Patrick Guy
    Resource Type:
    Masters Thesis
    Full Text:
    -known lower bounds for m < 90 on S3. 5 Other recent work has been contributed by P. W. Fowler and T

  • Advancing Power Grid Computing: Analysis and Synthesis of Wide-Area Data, Distributed Real-Time Hardware-In-the-Loop Simulations, and Machine Learning for Event Detection

    Creator:
    Hagan, Travis
    Abstract:
    Global Positioning Systems have allowed for precise timing of power system measurements over wide areas. This newly found capability has the potential to provide much greater insight into the operation of the power system and its response to contingencies, but few analytical techniques currently exist that provide enough robustness and...
    Resource Type:
    Dissertation
    Full Text:
    &#IHKL#LJPQGRS# T� � ' ����� �� � � �����U��++������ ��������� � �!��"� ��T������V�� ��� ���#O

  • Knowledge Base Completion Leveraging Natural Language Data

    Creator:
    Shahbazi, Hamed
    Abstract:
    Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
    Resource Type:
    Dissertation
    Full Text:
    for candidate yij as f (c) ij = [y T ijBci; y T ijB; ci], where B is a learned Rd ×Rd matrix. Note

  • Event detection with forward-backward recurrent neural networks

    Creator:
    Ghaeini, Mohammad Reza
    Abstract:
    Automatic event extraction from natural text is an important and challenging task for natural language understanding. Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on...
    Resource Type:
    Masters Thesis
    Full Text:
    ) at time step t is as follows: ht = f(Wxt + Uht−1 + b) (2.3) where W , U are the weights, b is the

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
    Full Text:
    al. (2017a) [60], we also decompose the score of a tree t to be the sum of the span scores: s(t

  • Improving and Understanding Deep Models for Natural Language Comprehension

    Creator:
    Ghaeini, Reza
    Abstract:
    Natural Language Comprehension is a challenging domain of Natural Language Processing. To improve a model’s language comprehension/understanding, one approach would be to enrich the structure of the model to enhance its capability in learning the latent rules of the language. In this dissertation, we will first introduce several deep models...
    Resource Type:
    Dissertation
    Full Text:
    t is as follows: ht = f(Wxt + Uht−1 + b) (2.3) where W , U are the weights, b is the bias, f

  • A Study of Temporal Convolutional Neural Networks for Music Language Modeling as Applied to Automatic Music Transcription

    Creator:
    Meyn, Matthew
    Abstract:
    Automatic music transcription (AMT) is the task, given an acoustic representation of music, to recover a symbolic notation of the written notes expressed by the sound. Transcribing music with multiple notes sounding simultaneously is difficult for both humans and machines. Much existing work on AMT has focused on suitable acoustic...
    Resource Type:
    Capstone Project
    Full Text:
    ) which means that the essential task is to estimate, for each event yt occurring at time t: P (yt|y1, y2

  • 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
    Full Text:
    one sentence “fruit flies fly.”. The simplified tagging set T = {N,V, .}. The simplified feature