Diffusion differentiable resampling
By: Jennifer Rosina Andersson, Zheng Zhao
Potential Business Impact:
Makes computer predictions more accurate with less data.
This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). We propose a new informative resampling method that is instantly pathwise differentiable, based on an ensemble score diffusion model. We prove that our diffusion resampling method provides a consistent estimate to the resampling distribution, and we show by experiments that it outperforms the state-of-the-art differentiable resampling methods when used for stochastic filtering and parameter estimation.
Similar Papers
Diffusion Models are Molecular Dynamics Simulators
Machine Learning (CS)
Simulates molecules moving like real life.
Diffusion Models are Molecular Dynamics Simulators
Machine Learning (CS)
Simulates molecules by learning from pictures.
Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey
Machine Learning (CS)
Guides computers to solve hard problems using smart guessing.