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 differentiable, based on an ensemble score diffusion model. We theoretically prove that our diffusion resampling method provides a consistent resampling distribution, and we show empirically that it outperforms the state-of-the-art differentiable resampling methods on multiple filtering and parameter estimation benchmarks. Finally, we show that it achieves competitive end-to-end performance when used in learning a complex dynamics-decoder model with high-dimensional image observations.

翻译：本文研究序贯蒙特卡罗方法（如粒子滤波）中的可微分重采样问题。我们提出了一种基于集成分数扩散模型的新型信息重采样方法，该方法具有即时可微性。我们从理论上证明了所提出的扩散重采样方法能够提供一致的重采样分布，并通过实验表明该方法在多个滤波与参数估计基准测试中优于当前最先进的可微分重采样方法。最后，我们展示了该方法在基于高维图像观测学习复杂动态解码器模型时，能够取得具有竞争力的端到端性能。