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Poster
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Alexander Neitz · Giambattista Parascandolo · Stefan Bauer · Bernhard Schölkopf

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #150
in Wed Poster Session B »

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.

Keywords: Predictive Models · Model-Based RL
[ Paper] 

Author Information

Alexander Neitz (Max Planck Institute for Intelligent Systems)
Giambattista Parascandolo (Max Planck Insitute for Intelligent Systems & ETH)
Stefan Bauer (MPI for Intelligent Systems)
Bernhard Schölkopf (MPI for Intelligent Systems)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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