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Filtering by: Creator Tuan, Le Anh Remove constraint Creator: Tuan, Le Anh Advisor Raich, Raviv Remove constraint Advisor: 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:
    , chi Van Anh, anh Trung, Trang, Dieu Anh, Trang Tran, Tuan, chi Truc, chi Linh, Thai, anh Duong, Huong

  • Weak-supervision Time-series Analysis

    Creator:
    You, Zeyu
    Abstract:
    Efficient time-series analysis can impact multiple application domains such as motif discovery in gene analysis or music data, extracting spectro-temporal patterns in acoustic scene analysis, or annotating and classifying electrical bio-signals (such as ECG, EEG, and EMG) for medical applications. Time-series analysis involves a variety of tasks. To predict future...
    Resource Type:
    Dissertation
    Full Text:
    - lished papers. At last but not least, I want to thank our group members, Anh Pham, Tam Nguyen Thi, Thi

  • A statistical inference framework for finding recurring patterns in large data with applications to energy management

    Creator:
    You, Zeyu
    Abstract:
    We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images or a short DNA code that is repeated across multiple DNA sequences. We first consider a simple problem of finding a single unknown pattern in...
    Resource Type:
    Masters Thesis
    Full Text:
    members, Evgenia Chunikhina, Anh Pham, Sumanth Avadhani and Xin Li, for their support on my thesis

  • Convergence Analysis Framework for Fixed-Point Algorithms in Machine Learning and Signal Processing

    Creator:
    Vu, Trung
    Abstract:
    Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...
    Resource Type:
    Dissertation
    Full Text:
    include Anh Pham, Evgenia Chunikhina, Zeyu You, Tam Nguyen, Phung Lai, Alan Campbell, Hector Dominguez