Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training. However, they are often prohibitively more expensive from a computational point of view and cannot be applied to maximum likelihood training in a scalable manner, which severely hinders their widespread adoption. In this work, we overcome these crucial limitations. Specifically, we propose a fast path gradient estimator which improves computational efficiency significantly and works for all normalizing flow architectures of practical relevance. We then show that this estimator can also be applied to maximum likelihood training for which it has a regularizing effect as it can take the form of a given target energy function into account. We empirically establish its superior performance and reduced variance for several natural sciences applications.

我们提出了一种快速路径梯度估计器，它显著提高了计算效率，并适用于所有实际相关的标准化流体结构。我们还证明了该估计器可以应用于最大似然训练，并且在几个自然科学应用中具有优越的性能和降低的方差。

快速统一的路径梯度估计方法用于正规化流