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Neural Network Optimization

3,649 papers

Papers per year

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Papers

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives NIPS 2014
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions NIPS 2014
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators NIPS 2014
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights NIPS 2014
On the Computational Efficiency of Training Neural Networks NIPS 2014
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System NIPS 2014
Fast Stochastic Alternating Direction Method of Multipliers ICML 2014
Max-Margin Infinite Hidden Markov Models ICML 2014
A Deep and Tractable Density Estimator ICML 2014
GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results ICML 2014
Learning by Stretching Deep Networks ICML 2014
Learning Polynomials with Neural Networks ICML 2014
Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression JMLR 2014
Natural Evolution Strategies JMLR 2014
Dropout: A Simple Way to Prevent Neural Networks from Overfitting JMLR 2014
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent NIPS 2013
Phase Retrieval using Alternating Minimization NIPS 2013
Linear Convergence with Condition Number Independent Access of Full Gradients NIPS 2013
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization NIPS 2013
Better Approximation and Faster Algorithm Using the Proximal Average NIPS 2013
Correlations strike back (again): the case of associative memory retrieval NIPS 2013
Understanding Dropout NIPS 2013
Adaptive dropout for training deep neural networks NIPS 2013
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NIPS 2013
Variance Reduction for Stochastic Gradient Optimization NIPS 2013