  We present a new approach to automatic amortized inference in universal
probabilistic programs which improves performance compared to current methods.
Our approach is a variation of inference compilation (IC) which leverages deep
neural networks to approximate a posterior distribution over latent variables
in a probabilistic program. A challenge with existing IC network architectures
is that they can fail to model long-range dependencies between latent
variables. To address this, we introduce an attention mechanism that attends to
the most salient variables previously sampled in the execution of a
probabilistic program. We demonstrate that the addition of attention allows the
proposal distributions to better match the true posterior, enhancing inference
about latent variables in simulators.
