We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The architecture shows close-to-optimal results on temporal modeling
of video data, competitive results on music modeling, and improved perceptual
grouping based on higher order abstractions, such as stochastic textures and
motion cues. We present results for fully supervised, semi-supervised, and
unsupervised tasks. The results suggest that the proposed architecture and
principles are powerful tools for learning a hierarchy of abstractions,
learning iterative inference and handling temporal information.

本文提出了一种基于递归的 Ladder 神经网络扩展，以此解决层次潜在变量模型中需要的推理问题，并证明该架构能够处理包括迭代推理和时间建模在内的各种复杂学习任务，涵盖视频数据的时间建模和提高基于高阶抽象的感知聚类等问题。作者在完全监督、半监督和非监督任务方面都取得了不错的结果，这表明所提出的架构和原则是学习抽象层次结构、学习迭代推理和处理时间信息的强有力工具。