title: Prediction Under Uncertainty with Error Encoding Networks
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.
authors: Mikael Henaff Junbo Zhao Yann Lecun
hIndex: Yann Lecun 120