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Filtering by: Creator Oslund, Robert S. Remove constraint Creator: Oslund, Robert S. Committee Member Fern, Xiaoli Remove constraint Committee Member: Fern, Xiaoli Degree Field Computer Science Remove constraint Degree Field: Computer Science Degree Level Doctoral Remove constraint Degree Level: Doctoral

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  • Creating, Understanding and Applying Machine Learning Models of Multiple Species

    Creator:
    Griffioen, Arwen Twinkle E.
    Abstract:
    Many problems in ecology and conservation biology can be formulated and solved using machine learning algorithms for multi-label classification. This dissertation addresses three topics related to predicting the distributions of multiple species. It improves existing methods and proposes a new modeling paradigm to address the multi-species, multi-label problem. The first...
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    Dissertation
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    . I’d like to thank my committee members, Matt Betts, Weng-Keen Wong, Xiaoli Fern and Robert Higdon

  • 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...
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    Dissertation
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    define, for the sets L ⊂ Z+ of numbered loci, S ⊂ Z+ of numbered SNPs, and R ⊂ S of numbered rSNPs. Then

  • 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...
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    Dissertation
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    reported in Inaguma et al. [46]. Pino et al. [73] use extra D′s,y which includes Librispeech (Ds,x) and

  • 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...
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    Dissertation
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    solution for path graphs. 107 4 MaxFirstPagePairs(S,L) algorithm uses a bottom-up CKY-approach to

  • 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...
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    Dissertation
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    problems under uncertainty. An MDP is a tuple (S,A, T,R, γ) where S is a finite state-space, A is a finite

  • 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
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    taken from ACE2005’s annotation, is also embedded starting with a random initialization. These

  • Machine learning for improving the quality of citizen science data

    Creator:
    Yu, Jun
    Abstract:
    Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute...
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    Dissertation
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    - based AUC are computed over S species. . . . . . . . . . . . . . . . . . . . 89 7.3 Difference in

  • Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

    Creator:
    Aziere, Nicolas
    Abstract:
    This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
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    Dissertation
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    3.2 The T at stage s enhances features of a query video segment X (s) i based on compressed

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
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    Dissertation
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    , r t i) } , t = 1 . . . Li and s t i, a t i, and rti are the state, action and reward at time t

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
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    Dissertation
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    M iterations of boosting are executed. In each iteration, for each class k, a set S(k) of