Global Convergence of Block Coordinate Descent in Deep Learning

1 March 2018

Papers citing "Global Convergence of Block Coordinate Descent in Deep Learning"

10 / 10 papers shown

Title
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness Shengkun Zhu Jinshan Zeng Sheng Wang Yuan Sun Xiaodong Li Yuan Yao Zhiyong Peng 44 0 0 23 Jul 2024
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee Chenyang Li Jihoon Chung Mengnan Du Haimin Wang Xianlian Zhou Bohao Shen 33 1 0 13 Mar 2023
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent Guanhua Ye Hongzhi Yin Tong Chen Miao Xu Quoc Viet Hung Nguyen Jiangning Song 39 9 0 17 Dec 2021
Stability and Generalization for Randomized Coordinate Descent Puyu Wang Liang Wu Yunwen Lei 18 7 0 17 Aug 2021
Coordinate descent on the orthogonal group for recurrent neural network training E. Massart V. Abrol 29 10 0 30 Jul 2021
Stochastic Block-ADMM for Training Deep Networks Saeed Khorram Xiao Fu Mohamad H. Danesh Zhongang Qi Li Fuxin 29 3 0 01 May 2021
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods Shiyu Duan José C. Príncipe MQ 20 3 0 09 Jan 2021
Implicit Deep Learning L. Ghaoui Fangda Gu Bertrand Travacca Armin Askari Alicia Y. Tsai AI4CE 34 176 0 17 Aug 2019
Lifted Neural Networks Armin Askari Geoffrey Negiar Rajiv Sambharya L. Ghaoui 26 37 0 03 May 2018
Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks Kensuke Nakamura Stefano Soatto Byung-Woo Hong BDL ODL 32 6 0 20 Nov 2017