Publications associées à Local SGD Converges Fast and Communicates Little

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Local SGD Converges Fast and Communicates Little

Graph Chatbot

Chattez avec Graph Search

Understanding generalization and robustness in modern deep learning

On the Generalization of Stochastic Gradient Descent with Momentum

Deep Learning Generalization with Limited and Noisy Labels

Phenomenological theory of variational quantum ground-state preparation

Communication-efficient distributed training of machine learning models

Universal and adaptive methods for robust stochastic optimization

Masked Training of Neural Networks with Partial Gradients

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Towards Understanding Sharpness-Aware Minimization

Understanding generalization and robustness in modern deep learning

Universal and adaptive methods for robust stochastic optimization

Deep Learning Generalization with Limited and Noisy Labels

Phenomenological theory of variational quantum ground-state preparation

Towards Understanding Sharpness-Aware Minimization

On the Generalization of Stochastic Gradient Descent with Momentum

Masked Training of Neural Networks with Partial Gradients

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Communication-efficient distributed training of machine learning models

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function