Continuous latent time series models are prevalent in Bayesian modeling;
examples include the Kalman filter, dynamic collaborative filtering, or dynamic
topic models. These models often benefit from structured, non mean field
variational approximations that capture correlations between time steps. Black
box variational inference with reparameterization gradients (BBVI) allows us to
explore a rich new class of Bayesian non-conjugate latent time series models;
however, a naive application of BBVI to such structured variational models
would scale quadratically in the number of time steps. We describe a BBVI
algorithm analogous to the forward-backward algorithm which instead scales
linearly in time. It allows us to efficiently sample from the variational
distribution and estimate the gradients of the ELBO. Finally, we show results
on the recently proposed dynamic word embedding model, which was trained using
our method.

介绍了一种类似于前向 - 后向算法的 BBVI 算法，它可以使得我们高效地从变分分布中进行采样，并且可以估计 ELBO 梯度。在动态词嵌入模型上的结果证明了我们方法的有效性。