ProdSumNet: reducing model parameters in deep neural networks via
product-of-sums matrix decompositions

v1v2 (latest)

ProdSumNet: reducing model parameters in deep neural networks via product-of-sums matrix decompositions

6 September 2018

ArXiv (abs)PDF HTML

Papers citing "ProdSumNet: reducing model parameters in deep neural networks via product-of-sums matrix decompositions"

8 / 8 papers shown

Title
$All-Optical Machine Learning Using Diffractive Deep Neural Networks$ All-Optical Machine Learning Using Diffractive Deep Neural Networks Xing Lin Y. Rivenson N. Yardimci Muhammed Veli Mona Jarrahi Aydogan Ozcan 103 1,668 0 14 Apr 2018
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices Caiwen Ding Siyu Liao Yanzhi Wang Zhe Li Ning Liu ... Yipeng Zhang Jian Tang Qinru Qiu Xinyu Lin Bo Yuan GNN 55 260 0 29 Aug 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,904 0 25 Aug 2017
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank Liang Zhao Siyu Liao Yanzhi Wang Zhe Li Jian Tang Victor Pan Bo Yuan 76 61 0 01 Mar 2017
A Kronecker-factored approximate Fisher matrix for convolution layers Roger C. Grosse James Martens ODL 105 264 0 03 Feb 2016
Deep Fried Convnets Zichao Yang Marcin Moczulski Misha Denil Nando de Freitas Alex Smola Le Song Ziyu Wang 102 267 0 22 Dec 2014
Fast Training of Convolutional Networks through FFTs Michaël Mathieu Mikael Henaff Yann LeCun 129 610 0 20 Dec 2013
Experiments with Random Projection S. Dasgupta 68 427 0 16 Jan 2013

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.