Publications associées à Stochastic Spectral Descent for Discrete Graphical Models

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

Stochastic Spectral Descent for Discrete Graphical Models

Graph Chatbot

Chattez avec Graph Search

Understanding generalization and robustness in modern deep learning

Phenomenological theory of variational quantum ground-state preparation

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

Metrizing Fairness

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Understanding generalization and robustness in modern deep learning

Universal and adaptive methods for robust stochastic optimization

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Masked Training of Neural Networks with Partial Gradients

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

Phenomenological theory of variational quantum ground-state preparation

Metrizing Fairness

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

Towards Understanding Sharpness-Aware Minimization

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate