… We propose using Normalizing Flows as a trainable kernel within the molecular dynamics
update of Hamiltonian Monte Carlo (HMC). By learning (invertible) transformations that …

… normalizing flows. We establish the expressive power of flow-based models, explain how to
use flows … This section doesn’t assume prior familiarity with normalizing flows, and can serve …

… automatic differentiation such as MALA and Hamiltonian Monte Carlo (HMC). • flowMC
uses state-of-the-art normalizing flow models such as Rational-Quadratic Splines to power its …

… ^4\) theory, the normalizing flow architectures, and the HMC component of the algorithm; in
… The motivation behind this work is that a Normalizing Flow parametrised by NNs ought to be …

… We compare deterministic normalizing flows using 5 blocks of 2 RealNVP layers with SNFs
that additionally use 20 Metropolis MC steps in each block totalling up to 100 MCMC steps in …

… space, these samples are used to train a normalizing flow (NF) model to approximate the
samples… flowMC supports gradient-based samplers such as MALA and HMC through automatic …

… for HMC ie, we run DLMC until we reach the virial threshold and then perform parallel HMC
… we propose to be evaluated using a Normalizing Flow determined by the positions of all the …

… Such maps can be constructed systematically by integrating certain flow equations in … flow
(which generates approximately trivializing maps for the Wilson gauge action) with the HMC …

… samples of the normalizing flows are comparable to the sample of a long run HMC.Details
on … ) Wasserstein distance WD(qη,p∗), and compare to samples of a long run HMC. For the …

… However, the vanilla HMC does not … normalizing flow (NF) models in the context of variational
inference (VI), which naturally enables the mini-batch training, as the alternative to HMC for …