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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84272

    Title: 次模深度壓縮感知用於空間搜尋;Spatial Search using Submodular Deep Compressed Sensing
    Authors: 蔡予中;Tsai, Yu-Chung
    Contributors: 數學系
    Keywords: 次模性;搜尋;壓縮感知;自動編碼;深度學習;Submodularity;Search;Compressed Sensing;Autoencoder;Deep Learning
    Date: 2020-07-20
    Issue Date: 2020-09-02 18:46:34 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 因次模函數的各式各樣應用(如物件搜尋、三維地圖重建),其已經引起人工智慧社群的注意,
    實驗證明此演算法比基準方案更有效率。;The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping).
    Searching for the victim is the key to search and rescue operations but finding an optimal search path is an NP-hard problem.
    Since the objective function of the spatial search is submodular, greedy algorithms can generate near-optimal solutions.
    However, learning submodular functions is a challenge since the number of a function′s outcomes of N sets is $2^N$.
    The state-of-the-art approach is based on compressed sensing techniques, which are to learn submodular functions in the Fourier domain and then recover the submodular functions in the spatial domain.
    However, the number of Fourier bases is relevant to the number of sets′ sensing overlapping.
    To overcome this issue, this research proposed a submodular deep compressed sensing (SDCS) approach to learning submodular functions.
    The algorithm consists of learning autoencoder networks and Fourier coefficients.
    The learned networks can be applied to predict
    $2^N$ values of submodular functions.
    Experiments conducted with this approach demonstrate that the algorithm is more efficient than the benchmark approach.
    Appears in Collections:[數學研究所] 博碩士論文

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