  Posterior inference in directed graphical models is commonly done using a
probabilistic encoder (a.k.a inference model) conditioned on the input. Often
this inference model is trained jointly with the probabilistic decoder (a.k.a
generator model). If probabilistic encoder encounters complexities during
training (e.g. suboptimal complxity or parameterization), then learning reaches
a suboptimal objective; a phenomena commonly called inference suboptimality. In
Variational Inference (VI), optimizing the ELBo using Stochastic Variational
Inference (SVI) can eliminate the inference suboptimality (as demonstrated in
this paper), however, this solution comes at a substantial computational cost
when inference needs to be done on new data points. Essentially, a long
sequential chain of gradient updates is required to fully optimize approximate
posteriors. In this paper, we present an approach called Pseudo-Encoded
Stochastic Variational Inference (PE-SVI), to reduce the inference complexity
of SVI during test time. Our approach relies on finding a suitable initial
start point for gradient operations, which naturally reduces the required
gradient steps. Furthermore, this initialization allows for adopting larger
step sizes (compared to random initialization used in SVI), which further
reduces the inference time complexity. PE-SVI reaches the same ELBo objective
as SVI using less than one percent of required steps, on average.
