Abstract
We describe a new probabilistic model for learning of coupled dynamical systems in latent state spaces. The coupling is achieved by combining predictions from several Gaussian process dynamical models in a product-of-experts fashion. Our approach facilitates modulation of coupling strengths without the need for computationally expensive re-learning of the dynamical models. We demonstrate the effectiveness of the new coupling model on synthetic toy examples and on high-dimensional human walking motion capture data.
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Velychko, D., Endres, D., Taubert, N., Giese, M.A. (2014). Coupling Gaussian Process Dynamical Models with Product-of-Experts Kernels. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_76
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DOI: https://doi.org/10.1007/978-3-319-11179-7_76
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