A Smoother Way to Train Structured Prediction Models

A Smoother Way to Train Structured Prediction Models

8 February 2019

Krishna Pillutla

Zaïd Harchaoui

Papers citing "A Smoother Way to Train Structured Prediction Models"

11 / 11 papers shown

Title
Stochastic Variance Reduction Methods for Saddle-Point Problems B. Palaniappan Francis R. Bach 115 212 0 20 May 2016
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 131 1,823 0 01 Jul 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 144 318 0 18 Feb 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 312 1,245 0 10 Sep 2013
Loopy Belief Propagation for Approximate Inference: An Empirical Study Kevin P. Murphy Yair Weiss Michael I. Jordan 3DV 121 1,882 0 23 Jan 2013
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark Schmidt Francis R. Bach 176 260 0 10 Dec 2012
An Efficient Message-Passing Algorithm for the M-Best MAP Problem Dhruv Batra 66 29 0 16 Oct 2012
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 174 1,033 0 10 Sep 2012
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets Nicolas Le Roux Mark Schmidt Francis R. Bach ODL 68 104 0 28 Feb 2012
Natural Language Processing (almost) from Scratch R. Collobert Jason Weston Léon Bottou Michael Karlen Koray Kavukcuoglu Pavel P. Kuksa 173 7,721 0 02 Mar 2011
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction Hal Daumé D. Marcu 98 279 0 04 Jul 2009