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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:
    , chi Van Anh, anh Trung, Trang, Dieu Anh, Trang Tran, Tuan, chi Truc, chi Linh, Thai, anh Duong, Huong

  • 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:
    AN ABSTRACT OF THE DISSERTATION OF Tam Nguyen for the

  • 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