Search Results for author: Leon Klein

Found 4 papers, 1 papers with code

Equivariant Flows: exact likelihood generative learning for symmetric densities.

no code implementations ICML 2020 Jonas Köhler, Leon Klein, Frank Noe

We provide a theoretical sufficient criterium showing that the distribution generated by \textit{equivariant} normalizing flows is invariant with respect to these symmetries by design.

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

1 code implementation NeurIPS 2023 Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka

Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

no code implementations3 Jun 2020 Jonas Köhler, Leon Klein, Frank Noé

We provide a theoretical sufficient criterion showing that the distribution generated by \textit{equivariant} normalizing flows is invariant with respect to these symmetries by design.

Equivariant Flows: sampling configurations for multi-body systems with symmetric energies

no code implementations2 Oct 2019 Jonas Köhler, Leon Klein, Frank Noé

Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest.

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