# Prediction Under Uncertainty with Error-Encoding Networks

arXiv: https://arxiv.org/abs/1711.04994 · HTML: https://mlsanity.com/paper/1711.04994/

## Key facts

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

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

- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2211.12509/) (2022)
- [Video Representation Learning with Joint-Embedding Predictive Architectures](https://mlsanity.com/paper/2412.10925/) (2024)
- [Learning to track environment state via predictive autoencoding](https://mlsanity.com/paper/2112.07745/) (2021)
- [Stochastic Attribute Modeling for Face Super-Resolution](https://mlsanity.com/paper/2207.07945/) (2022)
- [A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning](https://mlsanity.com/paper/2603.15411/) (2026)

## Abstract (arXiv)

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling components of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimensional latent variable which is fed into a forward model. Our method uses a supervised training objective which is fast and easy to train. We evaluate it in the context of video prediction on multiple datasets and show that it is able to consistently generate diverse predictions without the need for alternating minimization over a latent space or adversarial training.