# Recurrent Ladder Networks

arXiv: https://arxiv.org/abs/1707.09219 · HTML: https://mlsanity.com/paper/1707.09219/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [Compositional Scene Representation Learning via Reconstruction: A Survey](https://mlsanity.com/paper/2202.07135/) (2022)
- [SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2211.12509/) (2022)
- [MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks](https://mlsanity.com/paper/2203.09303/) (2022)
- [Knowledge-Guided Object Discovery with Acquired Deep Impressions](https://mlsanity.com/paper/2103.10611/) (2021)
- [A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing](https://mlsanity.com/paper/2306.04919/) (2023)

## Abstract (arXiv)

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.