Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""
Abstract
We propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1. To this end we show how a bi-linear model of transformations, such as a gated autoencoder, can be turned into a recurrent network, by training it to predict future frames from the current one and the inferred transformation using backprop-through-time. We also show how stacking multiple layers of gating units in a recurrent pyramid makes it possible to represent the ”syntax” of complicated time series, and that it can outperform standard recurrent neural networks in terms of prediction accuracy on a variety of tasks.
Cite
Text
Michalski et al. "Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""." Neural Information Processing Systems, 2014.Markdown
[Michalski et al. "Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/michalski2014neurips-modeling/)BibTeX
@inproceedings{michalski2014neurips-modeling,
title = {{Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""}},
author = {Michalski, Vincent and Memisevic, Roland and Konda, Kishore},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {1925-1933},
url = {https://mlanthology.org/neurips/2014/michalski2014neurips-modeling/}
}