Bayesian inference for high-dimensional inverse problems is computationally costly and
requires selecting a suitable prior distribution. Amortized variational inference addresses …

Calibrating land surface models and accurately quantifying their uncertainty are crucial for
improving the reliability of simulations of complex environmental processes. This, in turn …

Generative models have gained popularity for their potential applications in imaging
science, such as image reconstruction, posterior sampling and data sharing. Flow-based …

InvertibleNetworks. jl is a Julia package designed for the scalable implementation of
normalizing flows, a method for density estimation and sampling in high-dimensional …

In the context of machine learning for uncertainty quantification (UQ) of inverse problems: we
propose to first transform input observations using the adjoint. We demonstrate with two …

Can we build continuous generative models which generalize across scales, can be
evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually …

In inverse problems we aim to reconstruct some underlying signal of interest from potentially
corrupted and often ill-posed measurements. Classical optimization-based techniques …

Positron emission tomography (PET) is an important functional medical imaging technique
often used in the evaluation of certain brain disorders, whose reconstruction problem is ill …

Neural fields or implicit neural representations (INRs) have attracted significant attention in
machine learning and signal processing due to their efficient continuous representation of …

Uncertainty quantification of machine learning (ML) predictions is of key importance for the
widespread adoption of ML-enabled electromagnetic imaging. As ML inference is a …