3 papers
cs.LG2025
Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances in Flat Directions
Marcel Kühn, Bernd Rosenow
Stochastic Gradient Descent (SGD) has become a cornerstone of neural network optimization due to its computational efficiency and generalization capabilities. However, the gradient…
cs.LG2025
Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning
Max Staats, Matthias Thamm, Bernd Rosenow
Large annotated datasets inevitably contain noisy labels, which poses a major challenge for training deep neural networks as they easily memorize the labels. Noise-robust loss func…
cond-mat.mes-hall2024
Friedel oscillations in one-dimensional 4He
Bernd Rosenow, Adrian Del Maestro
One-dimensional bosonic systems, such as helium confined to nanopores, exhibit Luttinger liquid behavior characterized by density waves as collective excitations. We investigate th…