Prediction Under Uncertainty with Error-Encoding Networks
  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.
