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Filtering by: Creator Geier, Joel E. Remove constraint Creator: Geier, Joel E. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Sliding-mode control of the super maneuverable aircraft

    Creator:
    Koo, Chang Sul
    Abstract:
    In this thesis, A nonlinear methodology for the control of the highly maneuverable, high performance aircraft HARV (F-18) is studied by using sliding-mode control (SMC). This control law, which takes a continuous function when the input constraints are not considered, satisfies the reachability condition by which concerned states are driven...
    Resource Type:
    Dissertation
    Full Text:
    . Magaiia, Joel Davis, David J. Griffiths, and Prasad Tadepalli, for their time, valuable advice and help

  • Improving the Effectiveness of Answering Keyword Queries Using Effective Subsets

    Creator:
    Khodadadi, Abtin
    Abstract:
    Most database users do not know formal query languages, such as SQL, and prefer to express their information needs using usable query languages, such as keyword queries. Keyword queries, however, are inherently ambiguous and challenging for the database systems to understand and answer effectively. We propose a novel approach to...
    Resource Type:
    Capstone Project
    Full Text:
    Conference on Management of data, pages 1005–1010. ACM, 2009. [13] Joel Coffman and Alfred C Weaver. 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:
    an d A ta ri en v ir on m en ts . G am e Q B N S iz es O ri gi n al M M A ft er P ru

  • 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:
    Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 Entity-ELMo (E-ELMo

  • 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:
    activations of units in the encoding layer. We represent a QBN via a continuous multilayer encoder E, which

  • 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:
    - dicted output. Fixes consisting of an end (E) to a hairpin (H) have high average information Content

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
    Resource Type:
    Dissertation
    Full Text:
    ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 (a-e) are performance graphs of programs Yahtzee, SeqL (training set), S1,S2,S3 for λ = 0.75

  • 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

  • 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:
    ] proposed to consider a generalized version of MEI which is characterized as follows: x∗ = arg max x Eζy

  • 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:
    highly e↵ective for de- tecting and classifying biological sequences, challenges remain in extracting