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Conference Paper

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

MPS-Authors
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Neitz,  A.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Parascandolo,  G.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Bauer,  S.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf,  B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Neitz, A., Parascandolo, G., Bauer, S., & Schölkopf, B. (2019). Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 31 (pp. 9816-9826). Red Hook, NY: Curran Associates, Inc. Retrieved from https://papers.nips.cc/paper/8188-adaptive-skip-intervals-temporal-abstraction-for-recurrent-dynamical-models.


Cite as: https://hdl.handle.net/21.11116/0000-0003-8037-E
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