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In this paper we propose and study a technique to reduce the number of parameters and computation time in fully-connected layers of neural networks using Kronecker product, at a mild cost of the prediction quality.
Estimating linear restrictions on regression coefficients for multivariate normal distributions
Theodore Wilbur Anderson · 1951
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Roger A Horn and Charles R Johnson · 1991
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Approximation with Kronecker products
Charles F Van Loan and Nikos Pitsianis · 1993
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Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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On the best rank-1 and rank-(r1,r2,. . .,rn) approximation of higher-order tensors
Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle · 2000
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The ubiquitous kronecker product
Charles F Van Loan · 2000
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On best rank one approximation of tensors
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Efficient and accurate approximations of nonlinear convolutional networks
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Yu Zhang, Ekapol Chuangsuwanich, and James Glass
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