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    ARTICLE

    Parameters Compressing in Deep Learning

    Shiming He1, Zhuozhou Li1, Yangning Tang1, Zhuofan Liao1, Feng Li1, *, Se-Jung Lim2

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.06130

    Abstract With the popularity of deep learning tools in image decomposition and natural language processing, how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved. These parameters are huge and can be as many as millions. At present, a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure. These methods include matrix decomposition and tensor decomposition. To let vector take advance of the compressing performance of More >

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