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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