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Filtering by: Creator Nguyen, Thi Kim Anh Remove constraint Creator: Nguyen, Thi Kim Anh Contributor Raich, Raviv Remove constraint Contributor: Raich, Raviv

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  • 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:
    AN ABSTRACT OF THE DISSERTATION OF Anh Pham for the degree

  • 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:
    group: Thi Kim Phung Lai, Anh The Pham, Zeyu You, Yuanli Pei, Xingyi Li, Trung Vu. Thank you for

  • 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:
    AN ABSTRACT OF THE PROJECT REPORT OF Thi Kim Phung Lai for

  • 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:
    Pei, Zeyu You, Anh The Pham, Zeyu You, Thi Tam Nguyen, Thi Kim Phung Lai, Zhongang Qi, Jialin Yuan