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.

本文中介绍了一种名为Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT)的方法，该方法将序列Monte Carlo采样器与使用归一化流的变分推断结合在一起，以传输退火温度，使用KL散度进行优化。研究表明，CRAFT在Annealed Flow Transport Monte Carlo和基于马尔可夫链的随机归一化流等领域上有所提高，融入粒子MCMC，使得这种学习采样器在具有挑战性的格子场理论示例上可以获得令人印象深刻的准确结果。

连续重复退火流输运蒙特卡罗