Latest Articles for Gradient Descent

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.

Discover new methods for tackling complex optimization problems.

Combining adaptive control with meta-learning improves system performance under uncertainty.

Exploring methods for understanding quantum systems through maximum entropy inference.

Exploring improved learning rates in neural networks for scientific computing.

A new method improves neural network training using a hybrid optimization approach.

HOBOTAN efficiently tackles complex higher-order optimization problems using advanced computing techniques.

This article discusses enhancing VPINNs efficiency using Least Squares and gradient descent.

Examining how stability affects neural networks' effectiveness on unseen data.

A new approach enhances accuracy and efficiency in weather predictions using machine learning.

Learn how importance sampling improves model training efficiency and accuracy.

A new approach combines Decision Trees with neural networks for improved efficiency and accuracy.

Exploring a novel learning algorithm that better aligns with brain functions.

KSOS method improves analysis and prediction in dynamic systems using kernel techniques.

A look at recent methods to recover low-rank matrices with fewer observations.

A new approach enhances the accuracy of probabilistic classifiers in machine learning.

A look at kernel Kullback-Leibler divergence and its practical applications.

Examining how flexibility in models enhances predictive accuracy through dynamic adjustments.

New approaches in optimal control tackle complex systems and constraints using innovative techniques.

New method MEOW tackles unlearning sensitive data in LLMs without losing performance.

A new optimization method improves performance of spin-torque oscillators in computing.

Exploring matrix factorization methods in data distributed across clients.

A new method improving optimization with inexact gradients.