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

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 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 …

Score-based diffusion models synthesize samples by reversing a stochastic process that
diffuses data to noise, and are trained by minimizing a weighted combination of score …

We present Bit Diffusion: a simple and generic approach for generating discrete data with
continuous state and continuous time diffusion models. The main idea behind our approach …

Generative flows and diffusion models have been predominantly trained on ordinal data, for
example natural images. This paper introduces two extensions of flows and diffusion for …

It has long been established that predictive models can be transformed into lossless
compressors and vice versa. Incidentally, in recent years, the machine learning community …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Before we start thinking about (deep) generative modeling, let us consider a simple
example. Imagine we have trained a deep neural network that classifies images (x∈ ℤ D) of …

Built on deep networks, end-to-end optimized image compression has made impressive
progress in the past few years. Previous studies usually adopt a compressive auto-encoder …