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。

PAVI: 平板折旧变分推断

PAVI: Plate-Amortized Variational Inference

Massive language models are the core of modern NLP modeling and have been
shown to encode impressive amounts of commonsense and factual information.
However, that knowledge exists only within the latent parameters of the model,
inaccessible to inspection and interpretation, and even worse, factual
information memorized from the training corpora is likely to become stale as
the world changes. Knowledge stored as parameters will also inevitably exhibit
all of the biases inherent in the source materials. To address these problems,
we develop a neural language model that includes an explicit interface between
symbolically interpretable factual information and subsymbolic neural
knowledge. We show that this model dramatically improves performance on two
knowledge-intensive question-answering tasks. More interestingly, the model can
be updated without re-training by manipulating its symbolic representations. In
particular this model allows us to add new facts and overwrite existing ones in
ways that are not possible for earlier models.

该论文提出了一种神经语言模型，该模型在符号可解释事实信息和亚符号神经知识之间包括显式接口，可以通过操作其符号表示更新并覆盖已有的知识，进而显著提升知识密集型问题的问答性能。

以事实为专家：基于符号知识的可适应和可解释神经记忆

Facts as Experts: Adaptable and Interpretable Neural Memory over  Symbolic Knowledge

Monocular 3D reconstruction of deformable objects, such as human body parts,
has been typically approached by predicting parameters of heavyweight linear
models. In this paper, we demonstrate an alternative solution that is based on
the idea of encoding images into a latent non-linear representation of meshes.
The prior on 3D hand shapes is learned by training an autoencoder with
intrinsic graph convolutions performed in the spectral domain. The pre-trained
decoder acts as a non-linear statistical deformable model. The latent
parameters that reconstruct the shape and articulated pose of hands in the
image are predicted using an image encoder. We show that our system
reconstructs plausible meshes and operates in real-time. We evaluate the
quality of the mesh reconstructions produced by the decoder on a new dataset
and show latent space interpolation results. Our code, data, and models will be
made publicly available.

使用非线性潜在网格表示的手部形状和关节姿态，在照片上进行单目 3D 重建，该模型具有实时性能。