Change-of-variables (CoV) formulas allow to reduce complicated probability densities to
simpler ones by a learned transformation with tractable Jacobian determinant. They are thus …

Normalizing Flows are generative models that directly maximize the likelihood. Previously,
the design of normalizing flows was largely constrained by the need for analytical …

We present a pseudoreversible normalizing flow method for efficiently generating samples
of the state of a stochastic differential equation (SDE) with various initial distributions. The …

Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data.
However, real data is typically only supported on a lower-dimensional manifold leading the …

In this paper, we establish a connection between the parameterization of flow-based and
energy-based generative models, and present a new flow-based modeling approach called …

Training normalizing flow generative models can be challenging due to the need to
calculate computationally expensive determinants of Jacobians. This paper studies the …

Maximum likelihood training has favorable statistical properties and is popular for generative
modeling, especially with normalizing flows. On the other hand, generative autoencoders …

In this paper we propose Image Downscaling Assessment by Rate-Distortion (IDA-RD) a
novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image …

Second-order optimization has been shown to accelerate the training of deep neural
networks in many applications, often yielding faster progress per iteration on the training …

Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous
action spaces are typically formulated based on actor-critic frameworks and optimized …