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stochastic gradient descent

1091 papers

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Also known as

SGD

Co-occurring keywords

non-convex optimization (547) distributed learning (563) neural network optimization (1293) convergence rate (607) convex optimization (1321) neural network (6616) variance reduction (523) stochastic optimization (1060) convergence analysis (395) differential privacy (1016)

Papers

GCN meets GPU: Decoupling “When to Sample” from “How to Sample” NIPS 2020
Stochastic Approximate Gradient Descent via the Langevin Algorithm AAAI 2020
Anchor Box Optimization for Object Detection WACV 2020
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence NIPS 2019
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks NIPS 2019
Momentum-Based Variance Reduction in Non-Convex SGD NIPS 2019
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points NIPS 2019
New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019
Continuous-time Models for Stochastic Optimization Algorithms NIPS 2019
Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training EMNLP 2019
Making Asynchronous Stochastic Gradient Descent Work for Transformers EMNLP 2019
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019
Active Mini-Batch Sampling Using Repulsive Point Processes AAAI 2019
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data ICML 2019
Self-similar Epochs: Value in arrangement ICML 2019
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances ICML 2019
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication ICML 2019
SAGA with Arbitrary Sampling ICML 2019
SGD: General Analysis and Improved Rates ICML 2019
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression ICML 2019
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD ICML 2019
AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes ICML 2019
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization ICML 2019
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization ICML 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes ICML 2019