Annealed Importance Sampling (AIS) moves particles along a Markov chain from a tractable initial distribution to an intractable target distribution. The recently proposed Differentiable AIS (DAIS) (Geffner and Domke, 2021; Zhang et al., 2021) enables efficient optimization of the transition kernels of AIS and of the distributions. However, we observe a low effective sample size in DAIS, indicating degenerate distributions. We thus propose to extend DAIS by a resampling step inspired by Sequential Monte Carlo. Surprisingly, we find empirically-and can explain theoretically-that it is not necessary to differentiate through the resampling step which avoids gradient variance issues observed in similar approaches for Particle Filters (Maddison et al., 2017; Naesseth et al., 2018; Le et al., 2018).

翻译：退火重要性采样（AIS）沿马尔可夫链将粒子从易处理的初始分布移动到难处理的目标分布。最近提出的可微分AIS（DAIS）（Geffner和Domke, 2021; Zhang等, 2021）实现了对AIS转移核与分布的高效优化。然而，我们观察到DAIS中的有效样本量偏低，表明分布存在退化问题。为此，我们提出通过引入序贯蒙特卡洛中的重采样步骤来扩展DAIS。令人惊讶的是，我们通过实验发现并可从理论上解释：无需对重采样步骤进行微分——这避免了粒子滤波类似方法中观察到的梯度方差问题（Maddison等, 2017; Naesseth等, 2018; Le等, 2018）。