The challenge of overfitting, in which the model memorizes the training data and fails to
generalize to test data, has become increasingly significant in the training of large neural …

Abstract Sharpness-Aware Minimization (SAM) has been instrumental in improving deep
neural network training by minimizing both training loss and loss sharpness. Despite the …

Abstract Sharpness-Aware Minimization (SAM) is a recent optimization framework aiming to
improve the deep neural network generalization, through obtaining flatter (ie less sharp) …

In recent years, distributed stochastic algorithms have become increasingly useful in the
field of machine learning. However, similar to traditional stochastic algorithms, they face a …

Recently, there has been a surge in interest in developing optimization algorithms for
overparameterized models as achieving generalization is believed to require algorithms …

Sharpness-Aware Minimization (SAM) is a recent optimization framework aiming to improve
the deep neural network generalization, through obtaining flatter (ie less sharp) solutions. As …