Empirical Fisher

Empirical Fisher information, a matrix approximating the curvature of a loss function's landscape, is central to improving optimization algorithms for deep neural networks. Current research focuses on refining its estimation, particularly through improved diagonal approximations and leveraging techniques like Kronecker product decompositions to manage computational costs in large models. These advancements aim to enhance the efficiency and robustness of training, leading to better generalization performance and impacting various applications, including natural language processing and computer vision. The improved accuracy and efficiency of empirical Fisher approximations are key to developing more effective optimization strategies for deep learning.