High-performance optimization algorithms are essential in deep learning. However,
understanding the behavior of optimization (ie, learning process) remains challenging due …

Abstract Sharpness-Aware Minimization (SAM) is an optimizer that takes a descent step
based on the gradient at a perturbation $ y_t= x_t+\rho\frac {\nabla f (x_t)}{\lVert\nabla f …

Abstract Sharpness-Aware Minimization (SAM) is a recently proposed gradient-based
optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep …

Minimax optimization problems have attracted a lot of attention over the past few years, with
applications ranging from economics to machine learning. While advanced optimization …

Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its
surprising effectiveness in improving generalization performance. However, training neural …

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) has substantially improved the generalization of
neural networks under various settings. Despite the success, its effectiveness remains …

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 …

Despite the vast empirical evidence supporting the efficacy of adaptive optimization methods
in deep learning, their theoretical understanding is far from complete. This work introduces …

The management of wastewater treatment plant (WWTP) and the assessment of uncertainty
in its design are crucial from an environmental engineering perspective. One of the key …