Deep neural network approaches to inverse imaging problems have produced impressive
results in the last few years. In this survey paper, we consider the use of generative models …

Many generative models synthesize data by transforming a standard Gaussian random
variable using a deterministic neural network. Among these models are the Variational …

Accurate uncertainty quantification is a major challenge in deep learning, as neural
networks can make overconfident errors and assign high confidence predictions to out-of …

The field of multi-object tracking has recently seen a renewed interest in the good old
schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex …

We show that normalising flows become pathological when used to model targets whose
supports have complicated topologies. In this scenario, we prove that a flow must become …

Flow-based models are powerful tools for designing probabilistic models with tractable
density. This paper introduces Convex Potential Flows (CP-Flow), a natural and efficient …

The solution of inverse problems is of fundamental interest in medical and astronomical
imaging, geophysics as well as engineering and life sciences. Recent advances were made …

To develop a machine sound monitoring system, a method for detecting anomalous sound is
proposed. Exact likelihood estimation using Normalizing Flows is a promising technique for …

A recent line of work in high-dimensional statistics working under the Gaussian mixture
hypothesis has led to a number of results in the context of empirical risk minimization …

Over recent years, deep learning methods have become an increasingly popular choice for
solving tasks from the field of inverse problems. Many of these new data-driven methods …