  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.
