4 papers - avg viability 2.3
A novel amortized Bayesian inference approach for fast and scalable parameter estimation in complex dynamical systems like Kuramoto models.
This paper provides a theoretical analysis of No-U-Turn Sampler variants, focusing on convergence conditions and mixing times for Gaussian targets, with no mention of practical implementation or product potential.
Develop calibrated estimators for Bayesian inference to improve test-time guidance in diffusion models.
A novel machine-learning algorithm for efficient Bayesian inference in function spaces.