4 citations · 5 across the 5 of their papers we have counts for
5 papers
Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling
Jonathan Spence, Tobías I. Liaudat, Konstantinos Zygalakis +1
Markov chain Monte Carlo (MCMC) methods are fundamental to Bayesian computation, but can be computationally intensive, especially in high-dimensional settings. Push-forward generat…
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems
Bernardin Tamo Amougou, Marcelo Pereyra, Barbara Pascal
Image restoration problems are often ill-posed, leading to significant uncertainty in reconstructed images. Accurately quantifying this uncertainty is essential for the reliable in…
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems
Jasper M. Everink, Bernardin Tamo Amougou, Marcelo Pereyra
Most image restoration problems are ill-conditioned or ill-posed and hence involve significant uncertainty. Quantifying this uncertainty is crucial for reliably interpreting experi…
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
Tobías I. Liaudat, Matthijs Mars, Matthew A. Price +3
Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensi…
Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems
Julian Tachella, Marcelo Pereyra
Scientific imaging problems are often severely ill-posed, and hence have significant intrinsic uncertainty. Accurately quantifying the uncertainty in the solutions to such problems…