Neural Network Optimization

Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds NeurIPS 2016 Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters ICML 2016 Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes NeurIPS 2016 SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques NeurIPS 2016 Solving Ridge Regression using Sketched Preconditioned SVRG ICML 2016 Starting Small - Learning with Adaptive Sample Sizes ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization ICML 2016 Strongly-Typed Recurrent Neural Networks ICML 2016 Textual Entailment with Structured Attentions and Composition COLING 2016 Towards Automatically-Tuned Neural Networks AutoML 2016 Towards Stability and Optimality in Stochastic Gradient Descent AISTATS 2016 Train faster, generalize better: Stability of stochastic gradient descent ICML 2016 Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks NeurIPS 2016 Accelerated Mirror Descent in Continuous and Discrete Time NeurIPS 2015 Accelerated Proximal Gradient Methods for Nonconvex Programming NeurIPS 2015 A Lower Bound for the Optimization of Finite Sums ICML 2015 An Empirical Exploration of Recurrent Network Architectures ICML 2015 Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization NeurIPS 2015 A Universal Catalyst for First-Order Optimization NeurIPS 2015 Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions AISTATS 2015 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ICML 2015 Bayesian dark knowledge NeurIPS 2015 Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting NeurIPS 2015 Deep LAC: Deep Localization, Alignment and Classification for Fine-Grained Recognition CVPR 2015 Deep learning with Elastic Averaging SGD NeurIPS 2015