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A rising problem in the compression of Deep Neural Networks is how to reduce the number of parameters in convolutional kernels and the complexity of these layers by low-rank tensor approximation.
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Decompositions of a higher-order tensor in block terms – Part I and II
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O ( d log N ) O(d\log N) -quantics approximation of N N - d d tensors in high-dimensional numerical modeling
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Joseph M. Landsberg · 2012
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Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, and Dmitry Vetrov · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Stable low-rank tensor decomposition for compression of convolutional neural network
A.-H. Phan, K. Sobolev, K. Sozykin, D. Ermilov, J. Gusak, P. Tichavský, V. Glukhov, I. Oseledets, and A. Cichocki · 2020
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Low-Rank Tensor Ring Model for Completing Missing Visual Data
M Salman Asif and Ashley Prater-Bennette · 2020
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Robust low-rank tensor ring completion
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Hierarchical Tensor Ring Completion
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Hyperspectral images super-resolution via learning high-order coupled tensor ring representation
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X. Zhang, J. Zou, K. He, and J. Sun · 2016
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W. Wang, V. Aggarwal, and S. Aeron · 2017
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Sensitivity and generalization in neural networks: an empirical study
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Wide compression: Tensor ring nets
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A Novel Rank Selection Scheme in Tensor Ring Decomposition Based on Reinforcement Learning for Deep Neural Networks
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Computation of cnn’s sensitivity to input perturbation
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Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence
Meng Ding, Ting-Zhu Huang, Xi-Le Zhao, and Tian-Hui Ma · 2022
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