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：离散潜在空间模型近年来在深度变分推断中取得了与连续潜在空间模型相当的性能。尽管这类模型仍面临诸多实现挑战，但它们为潜在空间提供了更好的可解释性，同时能更直接地表达自然存在的离散现象。最新方法大多倾向于在离散潜在数据上分别训练极高维的先验模型，这本身便是具有挑战性的任务。本文提出了一种潜在数据模型，其中离散状态采用马尔可夫链形式，支持高效的端到端训练。我们通过建筑管理数据集及公开的电力变压器数据集对所提出生成模型的性能进行了评估。