Sharp Minimum

Sharp minima in the loss landscape of neural networks are a focus of current research, investigating their relationship to generalization performance and the efficiency of optimization algorithms. Studies explore how sharpness, often measured by Hessian eigenvalues, affects model generalization, particularly in federated learning and across diverse architectures like transformers. The ongoing debate centers on whether flatter or sharper minima yield better generalization, with recent work suggesting that the optimal sharpness may be data-dependent and influenced by factors like optimizer choice and learning rate. Understanding and controlling minima sharpness holds significant potential for improving the training and generalization capabilities of machine learning models.