@article{velychko_making_2018,
	title = {Making the {Coupled} {Gaussian} {Process} {Dynamical} {Model} {Modular} and {Scalable} with {Variational} {Approximations}},
	volume = {20},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1099-4300},
	url = {https://www.mdpi.com/1099-4300/20/10/724},
	doi = {10.3390/e20100724},
	abstract = {We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dyn...},
	language = {en},
	number = {10},
	urldate = {2026-02-27},
	journal = {Entropy},
	publisher = {publisher},
	author = {Velychko, Dmytro and Knopp, Benjamin and Endres, Dominik},
	month = sep,
	year = {2018},
	keywords = {Gaussian processes, modularity, movement primitives, variational methods},
	file = {Full Text PDF:C\:\\Users\\duoyi\\Zotero\\storage\\GPNGAS43\\Velychko et al. - 2018 - Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximat.pdf:application/pdf},
}