10 citations · 18 across the 6 of their papers we have counts for
Showing 2025Show all
2 papers · 1 filter
cs.LG2025★ 1 cited
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
Max Eissler, Tim Korjakow, Stefan Ganscha +3
Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other ma…
cs.LG2025
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Khaled Kahouli, Winfried Ripken, Stefan Gugler +3
Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative sampling process remains a significa…