10 citations · 12 across the 3 of their papers we have counts for
3 papers
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…
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller +1
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of dist…
Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
Stefan Chmiela, Huziel E. Sauceda, Alexandre Tkatchenko +1
We develop a combined machine learning (ML) and quantum mechanics approach that enables data-efficient reconstruction of flexible molecular force fields from high-level ab initio c…