Advances in deep learning have greatly improved structure prediction of molecules.
However, many macroscopic observations that are important for real-world applications are …

More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains
one of the most effective methods for marginal likelihood estimation. It relies on a sequence …

Advances in deep learning have greatly improved structure prediction of molecules.
However, many macroscopic observations that are important for real-world applications are …

Normalizing flows are tractable density models that can approximate complicated target
distributions, eg Boltzmann distributions of physical systems. However, current methods for …

Connecting optimal transport and variational inference, we present a principled and
systematic framework for sampling and generative modelling centred around divergences …

Abstract Annealed Importance Sampling (AIS) synthesizes weighted samples from an
intractable distribution given its unnormalized density function. This algorithm relies on a …

Many methods that build powerful variational distributions based on unadjusted Langevin
transitions exist. Most of these were developed using a wide range of different approaches …

Mutual information (MI) is a fundamental quantity in information theory and machine
learning. However, direct estimation of MI is intractable, even if the true joint probability …