Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Machine Learning
2018-12-14 v3 Machine Learning
Abstract
We introduce a method which enables a recurrent dynamics model to be temporally abstract. Our approach, which we call Adaptive Skip Intervals (ASI), is based on the observation that in many sequential prediction tasks, the exact time at which events occur is irrelevant to the underlying objective. Moreover, in many situations, there exist prediction intervals which result in particularly easy-to-predict transitions. We show that there are prediction tasks for which we gain both computational efficiency and prediction accuracy by allowing the model to make predictions at a sampling rate which it can choose itself.
Cite
@article{arxiv.1808.04768,
title = {Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models},
author = {Alexander Neitz and Giambattista Parascandolo and Stefan Bauer and Bernhard Schölkopf},
journal= {arXiv preprint arXiv:1808.04768},
year = {2018}
}