stochastic gradient descent

Stochastic Gradient and Langevin Processes ICML 2020 Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NeurIPS 2020 Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function NeurIPS 2020 Stochastic Nested Variance Reduction for Nonconvex Optimization JMLR 2020 Stochastic Optimization with Laggard Data Pipelines NeurIPS 2020 Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension ICML 2020 Temporal Network Embedding with High-Order Nonlinear Information AAAI 2020 The Break-Even Point on Optimization Trajectories of Deep Neural Networks ICLR 2020 The Complexity of Finding Stationary Points with Stochastic Gradient Descent ICML 2020 The Error-Feedback framework: SGD with Delayed Gradients JMLR 2020 The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent ICML 2020 The Implicit and Explicit Regularization Effects of Dropout ICML 2020 The Implicit Regularization of Stochastic Gradient Flow for Least Squares ICML 2020 The Local Elasticity of Neural Networks ICLR 2020 Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020 The Pitfalls of Simplicity Bias in Neural Networks NeurIPS 2020 Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NeurIPS 2020 To Relieve Your Headache of Training an MRF, Take AdVIL ICLR 2020 Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning NeurIPS 2020 Training Neural Networks for and by Interpolation ICML 2020 Transferring Optimality Across Data Distributions via Homotopy Methods ICLR 2020 Truncated Linear Regression in High Dimensions NeurIPS 2020 Understanding Gradient Clipping in Private SGD: A Geometric Perspective NeurIPS 2020 Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle ICML 2020 Variance Reduction With Sparse Gradients ICLR 2020