arXiv Machine Learning
Jun 19

Fisher-Geometric Sharpness and the Implicit Bias of SGD toward Flat Minima

arXiv:2606. 20469v1 Announce Type: new Abstract: A widely held intuition in deep learning is that stochastic gradient descent (SGD) implicitly favors flat minima and that flat minima generalize better, but standard Euclidean measures of flatness such as the trace or maximum eigenvalue of the loss Hessian are not invariant under reparametrizations that preserve the network function, which undermines the theoretical foundations of this narrative.

By Md Sakir Ahmed, Kumaresh Sarmah, Hemen Dutta
arXiv Machine Learning
Jul 16

How the Hessian-Spectrum of Neural Networks Depends on Data

arXiv:2607. 13631v1 Announce Type: new Abstract: The Hessian matrix is an important quantity of interest when it comes to studying the loss landscape and optimization dynamics in deep learning, as well as designing measures of generalization, second-order learning algorithms, etc.

By Jasraj Singh, Enea Monzio Compagnoni, Antonio Orvieto