gradient descent

Adaptive Three Operator Splitting ICML 2018 Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced NeurIPS 2018 Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations COLT 2018 An Estimate Sequence for Geodesically Convex Optimization COLT 2018 An Online Learning Approach to Generative Adversarial Networks ICLR 2018 A PID Controller Approach for Stochastic Optimization of Deep Networks CVPR 2018 Bilevel Programming for Hyperparameter Optimization and Meta-Learning ICML 2018 BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning CVPR 2018 Breaking the Span Assumption Yields Fast Finite-Sum Minimization NeurIPS 2018 Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice JMLR 2018 Catalyst for Gradient-based Nonconvex Optimization AISTATS 2018 Connecting Optimization and Regularization Paths NeurIPS 2018 Convergence Analysis of Gradient Descent for Eigenvector Computation IJCAI 2018 Convergence of Unregularized Online Learning Algorithms JMLR 2018 Decoupled Parallel Backpropagation with Convergence Guarantee ICML 2018 Decoupling Gradient-Like Learning Rules from Representations ICML 2018 Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance NeurIPS 2018 Direct Runge-Kutta Discretization Achieves Acceleration NeurIPS 2018 Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients ICML 2018 Dynamic Evaluation of Neural Sequence Models ICML 2018 Exact natural gradient in deep linear networks and its application to the nonlinear case NeurIPS 2018 Extreme Learning to Rank via Low Rank Assumption ICML 2018 Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow ICML 2018 Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis NeurIPS 2018 FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods AISTATS 2018