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Prediction Under Uncertainty with Error Encoding Networks
Mikael Henaff, Junbo Zhao, Yann Lecun
Feb 15, 2018 (modified: Feb 15, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
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.
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