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In this work, we tackle model efficiency by exploiting redundancy in the \textit{implicit structure} of the building blocks of convolutional neural networks.
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Gilbert Strang · 1986
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Anders Krogh and John A Hertz · 1992
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Tensor-train decomposition
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Earlier work this paper cites.
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Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, et al · 2015
Earlier work this paper cites.
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