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cs.LG2026
Large Spikes in Stochastic Gradient Descent: A Large-Deviations View
Benjamin Gess, Daniel Heydecker
Large loss spikes in stochastic gradient descent are studied through a rigorous large-deviations analysis for a shallow, fully connected network in the NTK scaling. In contrast to…
cs.LG2025
THINNs: Thermodynamically Informed Neural Networks
Javier Castro, Benjamin Gess
Physics-Informed Neural Networks (PINNs) are a class of deep learning models aiming to approximate solutions of PDEs by training neural networks to minimize the residual of the equ…
cs.LG2025
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
Benjamin Gess, Sebastian Kassing, Nimit Rana
We give quantitative estimates for the rate of convergence of Riemannian stochastic gradient descent (RSGD) to Riemannian gradient flow and to a diffusion process, the so-called Ri…