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stochastic gradient descent

1088 papers

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Also known as

SGD

Co-occurring keywords

non-convex optimization (546) distributed learning (563) neural network optimization (1293) convergence rate (606) convex optimization (1320) neural network (6616) variance reduction (520) stochastic optimization (1060) convergence analysis (394) differential privacy (1010)

Papers

The Hierarchical Adaptive Forgetting Variational Filter ICML 2018
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization ICML 2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption ICML 2018
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning ICML 2018
An Alternative View: When Does SGD Escape Local Minima? ICML 2018
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations ICML 2018
Stability and Generalization of Learning Algorithms that Converge to Global Optima ICML 2018
The Hidden Vulnerability of Distributed Learning in Byzantium ICML 2018
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods ICML 2018
Data-Dependent Stability of Stochastic Gradient Descent ICML 2018
Are ResNets Provably Better than Linear Predictors? NIPS 2018
The Lingering of Gradients: How to Reuse Gradients Over Time NIPS 2018
Breaking the Span Assumption Yields Fast Finite-Sum Minimization NIPS 2018
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks NIPS 2018
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network NIPS 2018
The Convergence of Sparsified Gradient Methods NIPS 2018
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data NIPS 2018
L4: Practical loss-based stepsize adaptation for deep learning NIPS 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NIPS 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator NIPS 2018
Learning with SGD and Random Features NIPS 2018
Training Deep Models Faster with Robust, Approximate Importance Sampling NIPS 2018
Byzantine Stochastic Gradient Descent NIPS 2018
NEON2: Finding Local Minima via First-Order Oracles NIPS 2018
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD NIPS 2018