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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…
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer, Frank Noé
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimensi…
VAMPnets: Deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu +1
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throu…
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Hao Wu, Feliks Nüske, Fabian Paul +3
Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of…