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Neural Network Optimization

3,649 papers

Papers per year

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Papers

Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NIPS 2013
The Fast Convergence of Incremental PCA NIPS 2013
Adaptive Step-Size for Policy Gradient Methods NIPS 2013
Fast dropout training ICML 2013
On the importance of initialization and momentum in deep learning ICML 2013
On the difficulty of training recurrent neural networks ICML 2013
Maxout Networks ICML 2013
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes NIPS 2012
Feature Clustering for Accelerating Parallel Coordinate Descent NIPS 2012
Large Scale Distributed Deep Networks NIPS 2012
Scalable nonconvex inexact proximal splitting NIPS 2012
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NIPS 2012
A quasi-Newton proximal splitting method NIPS 2012
Deep Learning Made Easier by Linear Transformations in Perceptrons AISTATS 2012
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification AISTATS 2012
Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent? COLT 2012
Nearest Neighbor based Greedy Coordinate Descent NIPS 2011
Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent NIPS 2011
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NIPS 2011
Algorithms for Hyper-Parameter Optimization NIPS 2011
Better Mini-Batch Algorithms via Accelerated Gradient Methods NIPS 2011
A Convergence Analysis of Log-Linear Training NIPS 2011
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation JMLR 2011
Smoothness, Low Noise and Fast Rates NIPS 2010
An analysis on negative curvature induced by singularity in multi-layer neural-network learning NIPS 2010