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Filtering by: Creator Sobsey, Mark D. Remove constraint Creator: Sobsey, Mark D. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Peer Reviewed No Remove constraint Peer Reviewed: No

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  • 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:
    nodes to the premise tree nodes. The blue dashed lines mark the entailment relations, and the red

  • Long Wave Amplification in a Coral-Reef Lagoon

    Creator:
    Bheeroo, Vivek Anand
    Abstract:
    A series of laboratory experiments were conducted to study the wave field in the inner lagoon excited by ‘long’ incident waves. Three cases were considered: Cases A, B and C presenting incident waves of wavelength with factors of 1, 2 and 2.5 times the width of the reef respectively. The...
    Resource Type:
    Masters Thesis
    Full Text:
    (d) : noise during termination of wave maker ……………………..………….…………33 Fig 4.2: Le Mehaute (1976

  • A Benchmarking Study of Unsupervised Anomaly Detection Algorithms

    Creator:
    Emmott, Andrew F.
    Abstract:
    It is common practice in the unsupervised anomaly detection literature to create experimental benchmarks by sampling from existing supervised learning datasets. We seek to improve this practice by identifying four dimensions important to real-world anomaly detection applications --- point difficulty, clusteredness of anomalies, relevance of features, and relative frequency of...
    Resource Type:
    Masters Thesis
    Full Text:
    simulate and control these factors when sampling points during bench- mark creation. We apply this

  • A factoring approach for probabilistic inference in belief networks

    Creator:
    Li, Zhaoyu
    Abstract:
    Reasoning about any realistic domain always involves a degree of uncertainty. Probabilistic inference in belief networks is one effective way of reasoning under uncertainty. Efficiency is critical in applying this technique, and many researchers have been working on this topic. This thesis is the report of our research in this...
    Resource Type:
    Dissertation
    Full Text:
    =1)=0.8 p(c=11a=0)=0.3 p(dla,b): p(d=11a=1,b=1)=0.7 p(d=11a=1,b=0)=0.5 p(d=11a=0,b=1)=0.5 p(d=11a=0,b

  • Using Change Representations to Analyze Properties of Variational Software

    Creator:
    Wang, Xiangyu
    Abstract:
    Appropriate representations of variational software simplify the analysis of their properties.This thesis proposes tailored representations of two kinds variational softwares: difference files of merge commits in Git and feature models. For the former, we use the Choice Edit Model, which is based on the choice calculus, to represent changes introduced...
    Resource Type:
    Masters Thesis
    Full Text:
    syntax of the choice calculus can be found in [17]. e ::= ε | D | D〈e,e〉 | e...e The syntax says a

  • 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:
    and regret . . . . . . . . . . . . . . . . . . . . . . . . 68 4.6.5 Selecting σd

  • Machine learning methods for public policy : simulation, optimization, and visualization

    Creator:
    McGregor, Sean
    Abstract:
    Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for...
    Resource Type:
    Dissertation

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation
    Full Text:
    assumption applied to G [23]. 7 1.2.2 Constructing a causal network Let us assume we have a dataset D

  • 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:
    ) and (b,d,f) represent the normalized attention weights for Entailment, Neutral, and Contradiction

  • Simulator-Defined MDP Planning with Applications in Natural Resource Management

    Creator:
    Alkaee Taleghan, Majid
    Abstract:
    This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
    Resource Type:
    Dissertation
    Full Text:
    would also like to thank Kim Hall, Professor H. Jo Albers, and Mark Crowley for their collaboration on