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.

1 code implementation • 26 Jan 2023 • Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé

Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness.

1 code implementation • 21 Mar 2022 • Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations.

no code implementations • NeurIPS 2021 • Jonas Köhler, Andreas Krämer, Frank Noé

In this work, we introduce a class of smooth mixture transformations working on both compact intervals and hypertori.

no code implementations • 11 Jul 2021 • Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer

Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted.

no code implementations • 14 Oct 2020 • Andreas Krämer, Jonas Köhler, Frank Noé

Many types of neural network layers rely on matrix properties such as invertibility or orthogonality.

no code implementations • 3 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.

1 code implementation • NeurIPS 2020 • Hao Wu, Jonas Köhler, Frank Noé

The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics.

1 code implementation • 15 Oct 2019 • Frederik Harder, Jonas Köhler, Max Welling, Mijung Park

Developing a differentially private deep learning algorithm is challenging, due to the difficulty in analyzing the sensitivity of objective functions that are typically used to train deep neural networks.

no code implementations • 2 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.

2 code implementations • 4 Dec 2018 • Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge.

2 code implementations • 14 Sep 2017 • Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling

Many areas of science and egineering deal with signals with other symmetries, such as rotation invariant data on the sphere.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.