Normalizing flows model probability distributions through an expressive tractable density.
They transform a simple base distribution, such as a Gaussian, through a sequence of …

Coupling-based normalizing flows (eg RealNVP) are a popular family of normalizing flow
architectures that work surprisingly well in practice. This calls for theoretical understanding …

Image anomaly detection aims to detect out-of-distribution instances. Most existing methods
treat anomaly detection as an unsupervised task because anomalous training data and …

Accelerated magnetic resonance (MR) imaging attempts to reduce acquisition time by
collecting data below the Nyquist rate. As an ill-posed inverse problem, many plausible …

Abstract Invertible Neural Networks (INN) have become established tools for the simulation
and generation of highly complex data. We propose a quantum-gate algorithm for a …

Generative modeling has opened new avenues for solving previously intractable materials
design problems. However, these new opportunities are accompanied by a drastic increase …

Abstract Anomaly detection of Positron Emission Tomography (PET) are important tasks for
clinical diagnosis and treatments. For 3D PET images, it is arduous for the annotations of …

In this paper, we find that the training of Normalizing Flows (NFs) are easily affected by the
outliers and a small number (or high dimensionality) of training samples. To solve this …

Designing CTLE of high-speed channels can be complicated and time consuming. To
alleviate this issue, this paper investigates the invertible neural networks (INNs) for inverse …

In this study, we introduce a method for estimating sound fields in reverberant environments
using a conditional invertible neural network (CINN). Sound field reconstruction can be …