stochastic gradient descent

Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent NeurIPS 2013 Training and Analysing Deep Recurrent Neural Networks NeurIPS 2013 Understanding Dropout NeurIPS 2013 Variance Reduction for Stochastic Gradient Optimization NeurIPS 2013 A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification AISTATS 2012 A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NeurIPS 2012 Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training JMLR 2012 Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers AISTATS 2012 ImageNet Classification with Deep Convolutional Neural Networks NeurIPS 2012 Krylov Subspace Descent for Deep Learning AISTATS 2012 Large Scale Distributed Deep Networks NeurIPS 2012 Multi Kernel Learning with Online-Batch Optimization JMLR 2012 Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent? COLT 2012 Stochastic Gradient Descent with Only One Projection NeurIPS 2012 Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences COLT 2012 Beating SGD: Learning SVMs in Sublinear Time NeurIPS 2011 Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent NeurIPS 2011 Learning a Distance Metric from a Network NeurIPS 2011 Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities NeurIPS 2011 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NeurIPS 2011 Relative Entropy Inverse Reinforcement Learning AISTATS 2011 Erratum: SGDQN is Less Careful than Expected JMLR 2010 Optimal Web-Scale Tiering as a Flow Problem NeurIPS 2010 Parallelized Stochastic Gradient Descent NeurIPS 2010 Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation NeurIPS 2009