We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows. The normalizing flows are directly trained to transport between annealing temperatures using a KL divergence for each transition. This optimization objective is itself estimated using the normalizing flow/SMC approximation. We show conceptually and using multiple empirical examples that CRAFT improves on Annealed Flow Transport Monte Carlo (Arbel et al., 2021), on which it builds and also on Markov chain Monte Carlo (MCMC) based Stochastic Normalizing Flows (Wu et al., 2020). By incorporating CRAFT within particle MCMC, we show that such learnt samplers can achieve impressively accurate results on a challenging lattice field theory example.

翻译：我们提出了连续重复淬火流输运蒙特卡罗（CRAFT），它将顺序蒙特卡罗（SMC）采样器（本身是淬火重要性采样的广义形式）与使用归一化流的变分推理相结合。这些归一化流是直接训练以使用每个转换的KL散度在淬火温度之间进行传输的。这个优化目标本身使用归一化流/SMC逼近来估计。我们概念性地展示了CRAFT在Annealed Flow Transport Monte Carlo（Arbel等人，2021）和基于马尔可夫链蒙特卡罗（MCMC）的随机归一化流（Wu等人，2020）上的提升，并使用多个实证示例进行了验证。通过将CRAFT纳入粒子MCMC，我们展示了这样的学习采样器可以在一个具有挑战性的格子场理论示例中实现令人印象深刻的精度。