Search Results for author: Jonas Köhler

Found 11 papers, 4 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.

Flow-matching -- efficient coarse-graining molecular dynamics without forces

no code implementations21 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.

Smooth Normalizing Flows

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.

Generating stable molecules using imitation and reinforcement learning

no code implementations11 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.

Imitation Learning reinforcement-learning

Training Invertible Linear Layers through Rank-One Perturbations

no code implementations14 Oct 2020 Andreas Krämer, Jonas Köhler, Frank Noé

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

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.

Stochastic Normalizing Flows

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.

DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning

1 code implementation15 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.

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.

Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning

2 code implementations4 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.

Convolutional Networks for Spherical Signals

2 code implementations14 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.

General Classification Translation

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