Given some observed data and a probabilistic generative model, Bayesian
inference aims at obtaining the distribution of a model's latent parameters
that could have yielded the data. This task is challenging for large population
studies where thousands of measurements are performed over a cohort of hundreds
of subjects, resulting in a massive latent parameter space. This large
cardinality renders off-the-shelf Variational Inference (VI) computationally
impractical. In this work, we design structured VI families that can
efficiently tackle large population studies. To this end, our main idea is to
share the parameterization and learning across the different i.i.d. variables
in a generative model -symbolized by the model's plates. We name this concept
plate amortization, and illustrate the powerful synergies it entitles,
resulting in expressive, parsimoniously parameterized and orders of magnitude
faster to train large scale hierarchical variational distributions. We
illustrate the practical utility of PAVI through a challenging Neuroimaging
example featuring a million latent parameters, demonstrating a significant step
towards scalable and expressive Variational Inference.

本文提出使用结构化 VI 族来有效处理大量人口研究数据中的潜变量参数空间，并阐述 Plate Amortization 的思想可以加快训练，提高表达力，精简参数，在神经影像学实例中进行了应用，取得了可扩展性和可表达性 VI 的重要进展。