2 papers
cs.CE2026
Neural Markov chain Monte Carlo: Bayesian inversion via normalizing flows and variational autoencoders
Giacomo Bottacini, Matteo Torzoni, Andrea Manzoni
This paper introduces a Bayesian framework that combines Markov chain Monte Carlo (MCMC) sampling, dimensionality reduction, and neural density estimation to efficiently handle inv…
cs.LG2025
MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classification
Luca Caldera, Giacomo Bottacini, Lara Cavinato
Generative modeling has emerged as a powerful paradigm for representation learning, but its direct applicability to challenging fields like medical imaging remains limited: mere ge…