Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

Normalizing Flows are generative models which produce tractable distributions where both
sampling and density evaluation can be efficient and exact. The goal of this survey article is …

A class of generative models that unifies flow-based and diffusion-based methods is
introduced. These models extend the framework proposed in Albergo & Vanden-Eijnden …

Normalizing flows are a class of deep generative models that are especially interesting for
modeling probability distributions in physics, where the exact likelihood of flows allows …

Machine learning (ML) is transforming all areas of science. The complex and time-
consuming calculations in molecular simulations are particularly suitable for an ML …

INTRODUCTION Statistical mechanics aims to compute the average behavior of physical
systems on the basis of their microscopic constituents. For example, what is the probability …

Abstract Neural Networks (NNs) have provided state-of-the-art results for many challenging
machine learning tasks such as detection, regression and classification across the domains …

In many tasks, in particular in natural science, the goal is to determine hidden system
parameters from a set of measurements. Often, the forward process from parameter-to …

Normalizing flows are exact-likelihood generative neural networks which approximately
transform samples from a simple prior distribution to samples of the probability distribution of …

Abstract 3D human pose estimation from monocular images is a highly ill-posed problem
due to depth ambiguities and occlusions. Nonetheless, most existing works ignore these …