Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation

14 March 2019

Papers citing "Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation"

5 / 5 papers shown

Title
ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition Zhisheng Zhong Fangyin Wei Zhouchen Lin Chao Zhang 56 29 0 18 Jun 2019
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,883 0 25 Aug 2017
Tensorizing Neural Networks Alexander Novikov D. Podoprikhin A. Osokin Dmitry Vetrov 105 882 0 22 Sep 2015
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition V. Lebedev Yaroslav Ganin M. Rakhuba Ivan Oseledets Victor Lempitsky 63 884 0 19 Dec 2014
Speech Recognition with Deep Recurrent Neural Networks Alex Graves Abdel-rahman Mohamed Geoffrey E. Hinton 226 8,517 0 22 Mar 2013