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Filtering by: Creator Rosenberg, Noah A. Remove constraint Creator: Rosenberg, Noah A. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • LacEDAemon : a programming environment for the multiparadigm language leda

    Creator:
    Pandey, Rajeev K.
    Abstract:
    Multiparadigm programming languages are a recent development in the realm of programming languages. A multiparadigm programming language allows the use of multiple, differing programming paradigms without departing from a single, unified linguistic framework. Multiparadigm programming languages are claimed to have benefits to both pedagogy and complex application creation. The beneficial...
    Resource Type:
    Dissertation
    Full Text:
    Doctor of Philosophy in Computer Science presented on August 28, 1998. Title: LacEDAemon: A Programming

  • Converting English text to speech : a machine learning approach

    Creator:
    Bakiri, Ghulum
    Abstract:
    The task of mapping spelled English words into strings of phonemes and stresses ("reading aloud") has many practical applications. Several commercial systems perform this task by applying a knowledge base of expert-supplied letter-to-sound rules. This dissertation presents a set of machine learning methods for automatically constructing letter-to-sound rules by analyzing...
    Resource Type:
    Dissertation

  • On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data

    Creator:
    Roth, Mark
    Abstract:
    This paper discusses opportunities for developments in spatial clustering methods to help leverage broad scale community science data for building species distribution models (SDMs). SDMs are critical tools that inform the science and policy needed to mitigate the impacts of climate change on biodiversity. Community science data span spatial and...
    Resource Type:
    Capstone Project
    Full Text:
    Roth A PROJECT submitted to Oregon State University in

  • Species Distribution Modeling of Citizen Science Data as A Classification Problem with Class-Conditional Label Noise

    Creator:
    He, Liqiang
    Abstract:
    Species distribution models (SDM), which quantify the correlation between the distribution of a species and environmental factors, are increasingly used to map and monitor animal and plant distributions in the context of awareness of environmental change and its ecological consequence. For perfect data, this is a straightforward classification problem from...
    Resource Type:
    Masters Thesis
    Full Text:
    of Citizen Science Data as A Classification Problem with Class-Conditional Label Noise Abstract

  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
    Resource Type:
    Dissertation
    Full Text:
    Ramsey We describe a series of novel computational models, CERENKOV (Computational Elu- cidation of the

  • Machine Learning for Precision Medicine: Application to Cancer Chemotherapy Response Prediction and Wound Healing Status Assessment

    Creator:
    Wei, Qi
    Abstract:
    Ph.D. candidate Qi Wei's thesis consists of two projects: Chemotherapy Project: a study based on the research paper "Predicting chemotherapy response of various cancer types using a variational auto-encoder approach" submitted to the bioRxiv preprint archive and accepted by the BMC bioinformatics; and Wound Monitor Project: implementing and assessing analytics...
    Resource Type:
    Dissertation
    Full Text:
    Assessment Abstract approved: Stephen A. Ramsey Ph.D. candidate Qi Wei’s thesis consists of two projects

  • Fault-tolerant ring embedding in De Bruijn networks

    Creator:
    Rowley, Robert A.
    Resource Type:
    Dissertation
    Full Text:
    Abstract of the Thesis of Robert A. Rowley for the degree of Doctor of Philosophy in Computer Science

  • Revisiting output coding for sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this...
    Resource Type:
    Masters Thesis
    Full Text:
    converting a problem with many labels to a set of problems with binary labels. Models were independently

  • 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:
    . Fern Natural Language Comprehension is a challenging domain of Natural Language Pro- cessing. To

  • 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:
    Constraints for Sequential Decision Making Abstract approved: Alan P. Fern Writing a program that performs