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Filtering by: Advisor Fern, Xiaoli Z. Remove constraint Advisor: Fern, Xiaoli Z. Resource Type Masters Thesis Remove constraint Resource Type: Masters Thesis

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  • 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:
    Forward-Backward Recurrent Neural Networks Abstract approved: Xiaoli Z. Fern Automatic event extraction

  • Simultaneous segmentation and classification of bird song using CNN

    Creator:
    Narasimhan, Revathy
    Abstract:
    In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where the goal is to segment bird syllables from the recording and predict the bird species for the syllables. Traditional methods for this task addresses the...
    Resource Type:
    Masters Thesis
    Full Text:
    - lutional encoder-decoder architecture for image segmentation. 2015. [4] Forrest Briggs, Xiaoli Z. Fern

  • Supervised Hierarchical Segmentation for Bird Bioacoustics

    Creator:
    Tjahja, Teresa V.
    Abstract:
    Bioacoustics analysis can be used to conduct environmental monitoring by detecting the presence of birds species. This analysis usually involves identifying the species from their calls. In most frameworks, bird song syllables are extracted from audio recordings and individual syllables are input to a classifier to identify the species. Extraction...
    Resource Type:
    Masters Thesis
    Full Text:
    Abstract approved: Xiaoli Z. Fern Bioacoustics analysis can be used to conduct environmental monitoring

  • Learning greedy policies for the easy-first framework

    Creator:
    Xie, Jun
    Abstract:
    Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
    Resource Type:
    Masters Thesis
    Full Text:
    the Easy-First Framework Abstract approved: Xiaoli Z. Fern Easy-first, a search-based structured

  • Active learning of constraints for semi-supervised clustering

    Creator:
    Xiong, Sicheng
    Abstract:
    Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints. In this paper, we study the active learning problem of selecting pairwise must-link and cannot-link constraints for semisupervised clustering. We consider active learning in an iterative manner where in each iteration queries are...
    Resource Type:
    Masters Thesis
    Full Text:
    Constraints for Semi-Supervised Clustering Abstract approved: Xiaoli Z. Fern Semi-supervised clustering

  • Open Set Learning with Counterfactual Images

    Creator:
    Neal, Lawrence W.
    Abstract:
    In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
    Resource Type:
    Masters Thesis
    Full Text:
    Counterfactual Images Abstract approved: Fuxin Li Xiaoli Fern In open set recognition, a classifier must label

  • Learning multiple non-redundant codebooks with word clustering for document classification

    Creator:
    Surve, Akshat Sudhakar
    Abstract:
    The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
    Resource Type:
    Masters Thesis
    Full Text:
    : _____________________________________________________________________ Xiaoli Fern The problem of document classification has been widely studied in machine

  • An ensemble method for macrosoma prediction

    Creator:
    Qin, Lu
    Abstract:
    Macrosomia is a medical term describing a new baby born with an excessive birth weight (greater than 4000g). Fetal macrosomia may lead to both pregnancy complications, and increased risk of mother's and baby's health problems after birth. But the potential complications may be mitigated by a cesarean delivery. As such,...
    Resource Type:
    Masters Thesis
    Full Text:
    March 12, 2014. Title: An Ensemble Method for Macrosoma Prediction Abstract approved: Xiaoli Z