Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models

30 March 2020

Papers citing "Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models"

12 / 12 papers shown

Title
Vector-Valued Graph Trend Filtering with Non-Convex Penalties R. Varma Harlin Lee J. Kovacevic Yuejie Chi 36 33 0 29 May 2019
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity Hilal Asi John C. Duchi 102 124 0 12 Oct 2018
Stochastic model-based minimization of weakly convex functions Damek Davis Dmitriy Drusvyatskiy 58 373 0 17 Mar 2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption Lam M. Nguyen Phuong Ha Nguyen Marten van Dijk Peter Richtárik K. Scheinberg Martin Takáč 64 227 0 11 Feb 2018
Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs Adil Salim Pascal Bianchi W. Hachem 40 17 0 19 Dec 2017
Stochastic Nonconvex Optimization with Large Minibatches Weiran Wang Nathan Srebro 51 26 0 25 Sep 2017
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal Piotr Dollár Ross B. Girshick P. Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He 3DH 61 3,666 0 08 Jun 2017
Train longer, generalize better: closing the generalization gap in large batch training of neural networks Elad Hoffer Itay Hubara Daniel Soudry ODL 132 798 0 24 May 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox Jialei Wang Weiran Wang Nathan Srebro 72 54 0 21 Feb 2017
Towards stability and optimality in stochastic gradient descent Panos Toulis Dustin Tran E. Airoldi 48 56 0 10 May 2015
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Feng Niu Benjamin Recht Christopher Ré Stephen J. Wright 96 2,272 0 28 Jun 2011
Hybrid Deterministic-Stochastic Methods for Data Fitting M. Friedlander Mark Schmidt 93 387 0 13 Apr 2011