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 …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

This paper introduces a generative model equivariant to Euclidean symmetries: E (n)
Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E (n) …

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

In deep latent Gaussian models, the latent variable is generated by a time-inhomogeneous
Markov chain, where at each time step we pass the current state through a parametric …

Abstract We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a
latent second order ODE model for high-dimensional sequential data. Leveraging the …

Many scientific problems require to process data in the form of geometric graphs. Unlike
generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or …

The sampling of probability distributions specified up to a normalization constant is an
important problem in both machine learning and statistical mechanics. While classical …

A normalizing flow is an invertible mapping between an arbitrary probability distribution and
a standard normal distribution; it can be used for density estimation and statistical inference …

The Boltzmann distribution is a natural model for many systems, from brains to materials and
biomolecules, but is often of limited utility for fitting data because Monte Carlo algorithms are …