Optimization

Efficient First-Order Algorithms for Adaptive Signal Denoising ICML 2018 Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks ICML 2018 Exploring Recombination for Efficient Decoding of Neural Machine Translation EMNLP 2018 Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis NeurIPS 2018 Frank-Wolfe with Subsampling Oracle ICML 2018 GIANT: Globally Improved Approximate Newton Method for Distributed Optimization NeurIPS 2018 Global Optimality Conditions for Deep Neural Networks ICLR 2018 Gradient Hard Thresholding Pursuit JMLR 2018 Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models AISTATS 2018 How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? NeurIPS 2018 How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD NeurIPS 2018 Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation ACL 2018 ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing CVPR 2018 Joint Patch-Group Based Sparse Representation for Image Inpainting ACML 2018 Kronecker-factored Curvature Approximations for Recurrent Neural Networks ICLR 2018 L4: Practical loss-based stepsize adaptation for deep learning NeurIPS 2018 Learning Compact Neural Networks with Regularization ICML 2018 “Learning-Compression” Algorithms for Neural Net Pruning CVPR 2018 Learning Globally Optimized Object Detector via Policy Gradient CVPR 2018 Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking CVPR 2018 NEON2: Finding Local Minima via First-Order Oracles NeurIPS 2018 Neural Proximal Gradient Descent for Compressive Imaging NeurIPS 2018 Norm matters: efficient and accurate normalization schemes in deep networks NeurIPS 2018 On the Convergence of Adam and Beyond ICLR 2018 On the insufficiency of existing momentum schemes for Stochastic Optimization ICLR 2018