3 papers
math.OC2026
The Advective Fisher-Rao Geometry of Deterministic Measure Transport
Benjamin Gess, Johannes Müller
A novel advective Fisher-Rao metric is introduced for optimization tasks on paths of probability measures governed by the continuity equation. This metric is shown to lead to optim…
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…