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Filtering by: Creator Welter, Stephen C. Remove constraint Creator: Welter, Stephen C. Advisor Tadepalli, Prasad Remove constraint Advisor: Tadepalli, Prasad Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Causal Structure Learning from Experiments and Observations

    Creator:
    Sinha, Meghamala
    Abstract:
    In this research, we address the problem of learning a single causal network structure from multiple dataset generated from different experiments. The experiments can be observational or interventional. We assume that each dataset is generated by an unknown causal network altered under different experimental conditions (interventions, manipulation or perturbation). As...
    Resource Type:
    Masters Thesis
    Full Text:
    interventions. c©Copyright by Meghamala Sinha March 22, 2019 All Rights Reserved Causal Structure

  • Towards Direct Simultaneous Speech Translation

    Creator:
    Chen, Junkun
    Abstract:
    Simultaneous speech translation (SimulST) is widely useful in many cross-lingual communication scenarios, including multinational conferences and international traveling. Since text-based simultaneous machine translation (SimulMT) has achieved great success in recent years. The conventional cascaded approach for SimulST uses a pipeline of streaming ASR followed by simultaneous MT but suffers from...
    Resource Type:
    Dissertation
    Full Text:
    between (a) cascaded pipeline, (b) direct simultaneous ST, and (c) our ASR-assisted simultaneous ST. In (a

  • Towards Narrative Understanding with Deep Neural Networks and Hidden Markov Models

    Creator:
    Orr, J. Walker
    Abstract:
    Narratives are central to communication and the human experience. For a computer system to understand a narrative, it must be able to identify the key facts or plot elements that describe what happened or how the world has changed. These element are called events;establishing a document’s events and the relationships...
    Resource Type:
    Dissertation
    Full Text:
    informed baseline. c©Copyright by J. Walker Orr June 21 2019 All Rights Reserved Towards Narrative

  • 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:
    improves upon the responsiveness of the baseline that dispatches the nearest responders. c©Copyright by

  • High-Dimensional Reinforcement Learning with Human Feedback

    Creator:
    Curran, William
    Abstract:
    State-of-the-art personal robots need to perform complex manipulation tasks to be viable in complex scenarios. However, many of these robots, like the PR2, use manipulators with high degrees of freedom. High degrees of freedom are desirable from a functionality standpoint, but make the learning task more difficult by adding a...
    Resource Type:
    Dissertation
    Full Text:
    interface that uses this timeline interface paradigm. c©Copyright by William Curran June 5, 2017 All

  • Monte-Carlo planning for probabilistic domains

    Creator:
    Bjarnason, Ronald V.
    Abstract:
    This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyze how these algorithms can resolve some of the shortcomings of traditional probabilistic planning methods. The direct policy search...
    Resource Type:
    Dissertation
    Full Text:
    cards is known, solving more than 82% of games. c©Copyright by Ronald Vance Bjarnason, Jr. December