gradient descent

Gradient descent aligns the layers of deep linear networks ICLR 2019 Gradient Descent Finds Global Minima of Deep Neural Networks ICML 2019 Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds COLT 2019 Gradient Descent Provably Optimizes Over-parameterized Neural Networks ICLR 2019 h-detach: Modifying the LSTM Gradient Towards Better Optimization ICLR 2019 Heavy-ball Algorithms Always Escape Saddle Points IJCAI 2019 Implicit Regularization for Optimal Sparse Recovery NeurIPS 2019 Implicit Regularization in Deep Matrix Factorization NeurIPS 2019 Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks NeurIPS 2019 Is Deeper Better only when Shallow is Good? NeurIPS 2019 Iterative Least Trimmed Squares for Mixed Linear Regression NeurIPS 2019 Iterative Linearized Control: Stable Algorithms and Complexity Guarantees ICML 2019 Learning Deep Priors for Image Dehazing ICCV 2019 Learning One-hidden-layer ReLU Networks via Gradient Descent AISTATS 2019 Learning to Learn From Noisy Labeled Data CVPR 2019 Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models ICML 2019 Lipschitz Adaptivity with Multiple Learning Rates in Online Learning COLT 2019 Local Saddle Point Optimization: A Curvature Exploitation Approach AISTATS 2019 Matrix-Free Preconditioning in Online Learning ICML 2019 Maximum Mean Discrepancy Gradient Flow NeurIPS 2019 Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization AAAI 2019 Memory Efficient Adaptive Optimization NeurIPS 2019 MetaInit: Initializing learning by learning to initialize NeurIPS 2019 Meta-Learning with Implicit Gradients NeurIPS 2019 Meta-Learning with Latent Embedding Optimization ICLR 2019