3 papers
cs.LG2026
Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling
Jonathan Spence, TobÃas 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…
cs.CV2025
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…
cs.CV2025
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…