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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

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
Continuous-time Models for Stochastic Optimization Algorithms NIPS 2019
Exchangeability and Kernel Invariance in Trained MLPs IJCAI 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
Stagewise Training Accelerates Convergence of Testing Error Over SGD NIPS 2019
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019
Active Mini-Batch Sampling Using Repulsive Point Processes AAAI 2019
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects ICML 2019
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization ICML 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks ICML 2019
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study ICML 2019
SGD without Replacement: Sharper Rates for General Smooth Convex Functions ICML 2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization ICML 2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019
A Convergence Theory for Deep Learning via Over-Parameterization ICML 2019
Nostalgic Adam: Weighting More of the Past Gradients When Designing the Adaptive Learning Rate IJCAI 2019
Understanding and correcting pathologies in the training of learned optimizers ICML 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks NIPS 2019
Fine-grained Optimization of Deep Neural Networks NIPS 2019
Dimension-Free Bounds for Low-Precision Training NIPS 2019
Stochastic Gradient Push for Distributed Deep Learning ICML 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes ICML 2019