Latent State Inference in a Spatiotemporal Generative Model

dc.contributor.author	Karlbauer, Matthias
dc.contributor.author	Menge, Tobias
dc.contributor.author	Otte, Sebastian
dc.contributor.author	Lensch, Hendrik P. A.
dc.contributor.author	Scholten, Thomas
dc.contributor.author	Butz, Martin V.
dc.date.accessioned	2022-03-31T15:29:21Z
dc.date.available	2022-03-31T15:29:21Z
dc.date.issued	2021
dc.identifier.isbn	978-3-030-86379-1
dc.identifier.isbn	978-3-030-86380-7
dc.identifier.uri	http://hdl.handle.net/10900/125677
dc.language.iso	en	de_DE
dc.publisher	Cham: Springer	de_DE
dc.relation.ispartofseries	Lecture Notes in Computer Science;12894
dc.relation.uri	https://doi.org/10.1007/978-3-030-86380-7_31	de_DE
dc.subject.ddc	004	de_DE
dc.title	Latent State Inference in a Spatiotemporal Generative Model	de_DE
dc.type	BookPart	de_DE
utue.publikation.seiten	384-395	de_DE
utue.personen.roh	Karlbauer, Matthias
utue.personen.roh	Menge, Tobias
utue.personen.roh	Otte, Sebastian
utue.personen.roh	Lensch, Hendrik P. A.
utue.personen.roh	Scholten, Thomas
utue.personen.roh	Wulfmeyer, Volker
utue.personen.roh	Butz, Martin V.
utue.publikation.buchdesbeitrags	Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021	de_DE