Discrete latent space models have recently achieved performance on par with their continuous counterparts in deep variational inference. While they still face various implementation challenges, these models offer the opportunity for a better interpretation of latent spaces, as well as a more direct representation of naturally discrete phenomena. Most recent approaches propose to train separately very high-dimensional prior models on the discrete latent data which is a challenging task on its own. In this paper, we introduce a latent data model where the discrete state is a Markov chain, which allows fast end-to-end training. The performance of our generative model is assessed on a building management dataset and on the publicly available Electricity Transformer Dataset.

翻译：离散潜空间模型最近在深度变分推断中取得了与连续潜变量模型相当的性能。尽管它们仍面临各种实现挑战，但这些模型为更好地解释潜空间以及更直接地表示天然离散现象提供了机会。最近的多数方法提出在离散潜数据上分别训练非常高维度的先验模型，这本身是一项具有挑战性的任务。本文提出了一种潜数据模型，其中离散状态为马尔可夫链，从而支持快速的端到端训练。我们在一份建筑管理数据集以及公开可用的电力变压器数据集上评估了所提生成模型的性能。