Structured variational autoencoders (SVAEs) combine probabilistic graphical model priors on latent variables, deep neural networks to link latent variables to observed data, and structure-exploiting algorithms for approximate posterior inference. These models are particularly appealing for sequential data, where the prior can capture temporal dependencies. However, despite their conceptual elegance, SVAEs have proven difficult to implement, and more general approaches have been favored in practice. Here, we revisit SVAEs using modern machine learning tools and demonstrate their advantages over more general alternatives in terms of both accuracy and efficiency. First, we develop a modern implementation for hardware acceleration, parallelization, and automatic differentiation of the message passing algorithms at the core of the SVAE. Second, we show that by exploiting structure in the prior, the SVAE learns more accurate models and posterior distributions, which translate into improved performance on prediction tasks. Third, we show how the SVAE can naturally handle missing data, and we leverage this ability to develop a novel, self-supervised training approach. Altogether, these results show that the time is ripe to revisit structured variational autoencoders.

翻译：结构化变分自编码器（SVAE）结合了潜变量上的概率图模型先验、将潜变量与观测数据关联的深度神经网络，以及利用结构特性的近似后验推断算法。这类模型特别适用于序列数据，因其先验能够捕捉时间依赖关系。然而，尽管概念优雅，SVAE在实践中被证明难以实现，更通用的方法在实际应用中往往更受青睐。在此，我们利用现代机器学习工具重新审视SVAE，并展示其在准确性和效率方面相较于通用替代方案的优越性。首先，我们为SVAE核心的消息传递算法开发了现代实现，支持硬件加速、并行化及自动微分。其次，我们表明，通过利用先验中的结构，SVAE能够学习更精确的模型和后验分布，从而提升预测任务的性能。第三，我们展示了SVAE如何自然地处理缺失数据，并利用这一能力提出了新颖的自监督训练方法。综合这些结果，表明重新审视结构化变分自编码器的时机已经成熟。