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 …

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 …

As 3D point clouds become the representation of choice for multiple vision and graphics
applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds …

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 …

We introduce a new family of deep neural network models. Instead of specifying a discrete
sequence of hidden layers, we parameterize the derivative of the hidden state using a …

A promising class of generative models maps points from a simple distribution to a complex
distribution through an invertible neural network. Likelihood-based training of these models …

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 …

Although variational autoencoders (VAEs) represent a widely influential deep generative
model, many aspects of the underlying energy function remain poorly understood. In …

Normalizing flows and autoregressive models have been successfully combined to produce
state-of-the-art results in density estimation, via Masked Autoregressive Flows …