← Optimization & Theory
Machine Learning › Optimization & Theory ›

Neural Network Optimization

3648 directly classified papers

Papers per year

Papers

Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization NIPS 2014
A Deep and Tractable Density Estimator ICML 2014
Stochastic Proximal Gradient Descent with Acceleration Techniques NIPS 2014
GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results ICML 2014
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights NIPS 2014
Learning by Stretching Deep Networks ICML 2014
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators NIPS 2014
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization NIPS 2014
Natural Evolution Strategies JMLR 2014
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System NIPS 2014
Dropout: A Simple Way to Prevent Neural Networks from Overfitting JMLR 2014
Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression JMLR 2014
Learning Polynomials with Neural Networks ICML 2014
On the Computational Efficiency of Training Neural Networks NIPS 2014
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NIPS 2013
The Fast Convergence of Incremental PCA NIPS 2013
On the difficulty of training recurrent neural networks ICML 2013
On the importance of initialization and momentum in deep learning ICML 2013
Phase Retrieval using Alternating Minimization NIPS 2013
Better Approximation and Faster Algorithm Using the Proximal Average NIPS 2013
Understanding Dropout NIPS 2013
Variance Reduction for Stochastic Gradient Optimization NIPS 2013
Correlations strike back (again): the case of associative memory retrieval NIPS 2013
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NIPS 2013
Fast dropout training ICML 2013