  Differentiable annealed importance sampling (DAIS), proposed by Geffner &
Domke (2021) and Zhang et al. (2021), allows optimizing, among others, over the
initial distribution of AIS. In this paper, we show that, in the limit of many
transitions, DAIS minimizes the symmetrized KL divergence (Jensen-Shannon
divergence) between the initial and target distribution. Thus, DAIS can be seen
as a form of variational inference (VI) in that its initial distribution is a
parametric fit to an intractable target distribution. We empirically evaluate
the usefulness of the initial distribution as a variational distribution on
synthetic and real-world data, observing that it often provides more accurate
uncertainty estimates than standard VI (optimizing the reverse KL divergence),
importance weighted VI, and Markovian score climbing (optimizing the forward KL
divergence).
