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  • Multiple Instance Multiple Label Learning with Limited Supervision

    Creator:
    Nguyen, Tam
    Abstract:
    In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...
    Resource Type:
    Dissertation
    Full Text:
    degree of Doctor of Philosophy in Computer Science presented on May 25, 2021. Title: Multiple Instance

  • Leveraging Structures of the Data in Deep Learning

    Creator:
    Li, Xingyi
    Abstract:
    The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
    Resource Type:
    Dissertation
    Full Text:
    of Doctor of Philosophy in Computer Science presented on December 7, 2020. Title: Leveraging

  • Multiple Instance Learning for Histopathological Image Classification

    Creator:
    Lai, Phung
    Abstract:
    In histopathological image analysis, image classification as well as pattern detection play a crucial role in the diagnosis and treatment process since the goal is to not only differentiate cancer types but also identify cancerous manifestations. Fully supervised learning strategies tend to address these problems using manually annotated cancerous regions...
    Resource Type:
    Capstone Project
    Full Text:
    the degree of Master of Science in Computer Science presented on April 23, 2018. Title: Multiple

  • On Convex Dimensionality Reduction for Classification

    Creator:
    Tammineni, Tez Deepthi
    Abstract:
    Dimensionality reduction (DR) is an efficient approach to reduce the size of data by capturing the informative intrinsic features and discarding the noise. DR methods can be grouped through a variety of categories, e.g. supervised/ unsupervised, linear/non-linear or parametric/non-parametric. Objective function based methods can be grouped into convex and non...
    Resource Type:
    Capstone Project
    Full Text:
    Deepthi Tammineni for the degree of Master of Science in Computer Science presented on March, 10 2011

  • Weak-supervision : Probabilistic Models and Inference

    Creator:
    Pham, Anh
    Abstract:
    In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...
    Resource Type:
    Dissertation
    Full Text:
    Engineering Director of the School of Electrical Engineering and Computer Science Dean of the Graduate