1 citations · 1 across the 6 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
Gyeonghoon Ko, Juho Lee
Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. However, training requires sam…
cs.LG2026
Permutation-Symmetrized Diffusion for Unconditional Molecular Generation
Gyeonghoon Ko, Juho Lee
Permutation invariance is fundamental in molecular point-cloud generation, yet most diffusion models enforce it indirectly via permutation-equivariant networks on an ordered space.…
cs.LG2025★ 1 cited
Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Soohaeng Yoo Willow, Tae Hyeon Park, Gi Beom Sim +6
Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current ML…