Longitudinal datasets measured repeatedly over time from individual subjects,
arise in many biomedical, psychological, social, and other studies. A common
approach to analyse high-dimensional data that contains missing values is to
learn a low-dimensional representation using variational autoencoders (VAEs).
However, standard VAEs assume that the learnt representations are i.i.d., and
fail to capture the correlations between the data samples. We propose the
Longitudinal VAE (L-VAE), that uses a multi-output additive Gaussian process
(GP) prior to extend the VAE's capability to learn structured low-dimensional
representations imposed by auxiliary covariate information, and derive a new KL
divergence upper bound for such GPs. Our approach can simultaneously
accommodate both time-varying shared and random effects, produce structured
low-dimensional representations, disentangle effects of individual covariates
or their interactions, and achieve highly accurate predictive performance. We
compare our model against previous methods on synthetic as well as clinical
datasets, and demonstrate the state-of-the-art performance in data imputation,
reconstruction, and long-term prediction tasks.

该研究提出了一种称为长期 VAE（L-VAE）的方法，通过使用多输出加性高斯过程（GP）先验来扩展 VAE 的能力，从而实现在辅助协变量信息的约束下学习结构化低维表示， 以及导出了一种新的 KL 离散度上界。该方法可以同时适应时间变化的共享和随机效应，产生结构化低维表示，剥离个体协变量或其相互作用的影响，以及实现高度准确的预测性能。

纵向变分自编码器

Longitudinal Variational Autoencoder

Microtask crowdsourcing is increasingly critical to the creation of extremely
large datasets. As a result, crowd workers spend weeks or months repeating the
exact same tasks, making it necessary to understand their behavior over these
long periods of time. We utilize three large, longitudinal datasets of nine
million annotations collected from Amazon Mechanical Turk to examine claims
that workers fatigue or satisfice over these long periods, producing lower
quality work. We find that, contrary to these claims, workers are extremely
stable in their quality over the entire period. To understand whether workers
set their quality based on the task's requirements for acceptance, we then
perform an experiment where we vary the required quality for a large
crowdsourcing task. Workers did not adjust their quality based on the
acceptance threshold: workers who were above the threshold continued working at
their usual quality level, and workers below the threshold self-selected
themselves out of the task. Capitalizing on this consistency, we demonstrate
that it is possible to predict workers' long-term quality using just a glimpse
of their quality on the first five tasks.

本研究利用大规模的纵向数据集，研究了众包工作者在从事长时间的重复任务时的行为，发现他们的工作质量极其稳定，建立了一种预测工作者长期质量的方法。