Latest Articles for Gradient Descent

Researchers improve quantum circuit performance using innovative methods for better results.

A look into the essential theory behind deep learning models.

A new approach using neural networks enhances viscosity for high-order methods.

A novel approach to parameter estimation in ordinary differential equations using diffusion tempering.

Insights into gradient descent behavior and the Edge of Stability.

Exploring the connection between weight matrices and feature learning in neural networks.

Examining self-attention and gradient descent in transformer models.

A new method simplifies complex multiscale problems using gradient descent.

Explore how Adam improves deep learning model training and outperforms gradient descent.

This article discusses Stochastic Gradient Flow and its impact on model learning.

This article examines how neural networks improve predictions with small initial weights.

A look at how Linear Transformer Blocks improve language models through in-context learning.

A guide to improving associative memory using gradient descent methods.

An efficient method for fitting complex models using probabilistic data.

Explore how momentum boosts efficiency in training neural networks.

A new perspective on how neural networks learn features through expert-like paths.

Examining gradient descent in phase retrieval and its optimization challenges.

A look at tropical Fermat-Weber points and their applications in various fields.

Learn about ProjGD's advantages for low-rank matrix estimation.

Explore gradient flow techniques to enhance ResNet training and performance.

Exploring the role of gradient descent in stochastic optimization and its implications for sample size.

Examining optimization techniques for challenging convex functions in unique geometric spaces.

This article examines how noise can improve machine learning model performance during training.

This article examines deep linear networks and the impact of sharpness on training.

Introducing a new adaptive stepsize method to improve optimization efficiency.

Examining complex interactions in games with advanced mathematical methods.

A look at regularized algorithms and their impact on machine learning performance.

Examining the importance of smallest eigenvalue in NTK for neural network training.

A new method enhances training for neural networks solving partial differential equations.

This study reveals the properties and applications of normal matrices and balanced graphs.

A study on improving neural network training with non-differentiable activation functions.

A look into how linear networks learn and evolve during training.

Improving optimization methods through UCB in local Bayesian strategies.

New method improves efficiency in distributed minimax optimization problems.

A method to convert continuous data into a simpler, discrete form.

Investigating how neural networks learn features during training.

Learn how step size affects gradient descent in logistic regression.

Examining dynamic methods for optimizing machine learning model training.

A new approach for finding leading eigenvectors in complex matrices.

Control theory enhances optimization methods for better system performance across various fields.