2 papers
cs.LG2026
SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures
Julian Kranz, Davide Gallon, Steffen Dereich +1
We study gradient flows for loss landscapes of fully connected feedforward neural networks with commonly used continuously differentiable activation functions such as the logistic,…
math.OC2025
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
Arnulf Jentzen, Julian Kranz, Adrian Riekert
Averaging techniques such as Ruppert--Polyak averaging and exponential movering averaging (EMA) are powerful approaches to accelerate optimization procedures of stochastic gradient…