@inproceedings{velychko_coupling_2014,
	address = {Cham},
	title = {Coupling {Gaussian} {Process} {Dynamical} {Models} with {Product}-of-{Experts} {Kernels}},
	isbn = {978-3-319-11179-7},
	doi = {10.1007/978-3-319-11179-7_76},
	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.},
	language = {en},
	booktitle = {Artificial {Neural} {Networks} and {Machine} {Learning} – {ICANN} 2014},
	publisher = {Springer International Publishing},
	author = {Velychko, Dmytro and Endres, Dominik and Taubert, Nick and Giese, Martin A.},
	editor = {Wermter, Stefan and Weber, Cornelius and Duch, Włodzisław and Honkela, Timo and Koprinkova-Hristova, Petia and Magg, Sven and Palm, Günther and Villa, Alessandro E. P.},
	year = {2014},
	keywords = {Computer Graphics, Gaussian Process, Products of Experts},
	pages = {603--610},
}