A General Family of Stochastic Proximal Gradient Methods for Deep Learning

15 July 2020

Papers citing "A General Family of Stochastic Proximal Gradient Methods for Deep Learning"

7 / 7 papers shown

Title
Quantization Networks Jiwei Yang Xu Shen Jun Xing Xinmei Tian Houqiang Li Bing Deng Jianqiang Huang Xiansheng Hua MQ 53 342 0 21 Nov 2019
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks Guodong Zhang James Martens Roger C. Grosse ODL 74 124 0 27 May 2019
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation Jihun Yun A. Lozano Eunho Yang ODL 17 5 0 26 May 2019
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 54 116 0 03 Feb 2019
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 336 18,300 0 27 May 2016
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 71 1,538 0 22 Sep 2013
$High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence$ High-dimensional covariance estimation by minimizing $\ell_1$ -penalized log-determinant divergence Pradeep Ravikumar Martin J. Wainwright Garvesh Raskutti Bin Yu 193 874 0 21 Nov 2008