Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

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

Many domains of science have developed complex simulations to describe phenomena of
interest. While these simulations provide high-fidelity models, they are poorly suited for …

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 normalizing flow models a complex probability density as an invertible transformation of a
simple base density. Flows based on either coupling or autoregressive transforms both offer …

Normalizing flows are a powerful tool for building expressive distributions in high
dimensions. So far, most of the literature has concentrated on learning flows on Euclidean …

Two apparently unrelated fields—normalizing flows and causality—have recently received
considerable attention in the machine learning community. In this work, we highlight an …

Likelihood-free methods perform parameter inference in stochastic simulator models where
evaluating the likelihood is intractable but sampling synthetic data is possible. One class of …

The unfolding of detector effects is crucial for the comparison of data to theory predictions.
While traditional methods are limited to representing the data in a low number of …

Generative networks are opening new avenues in fast event generation for the LHC. We
show how generative flow networks can reach percent-level precision for kinematic …