Prediction Under Uncertainty with Error Encoding Networks

Mikael Henaff, Junbo Zhao, Yann Lecun

Abstract:

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 com-
ponents 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 the forward model. Our method uses a simple su-
pervised 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 consi-
tently generate diverse predictions without the need for alternating minimization
over a latent space or adversarial training.

TL;DR: A simple and easy to train method for multimodal prediction in time series.

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/prediction-under-uncertainty-with-error/code)