925 citations · 1.2k across the 10 of their papers we have counts for
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Training Invertible Linear Layers through Rank-One Perturbations
Andreas Krämer, Jonas Köhler, Frank Noé
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic o…
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noé
Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution…
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies
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…
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Frank Noé, Simon Olsson, Jonas Köhler +1
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independen…
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali +1
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories. After unsupervised tra…