Pavel Procházka

Forecasting Multiple Observables with SCROLL: Score-Trained Uncertainty for Stochastic Dynamics

This work composition the observables'likelihoods in per-task free-routed last-layer beliefs on a shared backbone absorbs unit-dependent loss scaling into likelihood parameters learned in the same gradient pass, and results land where theory puts them.