Investigating efficient learning and compostionality in generative LSTM networks

dc.contributor.author	Fabi, Sarah	de_DE
dc.contributor.author	Otte, Sebastian	de_DE
dc.contributor.author	Wiese, Jonas Gregor	de_DE
dc.contributor.author	Butz, Martin V.	de_DE
dc.date.accessioned	2021-12-10T10:23:16Z
dc.date.available	2021-12-10T10:23:16Z
dc.date.issued	2020	de_DE
dc.identifier.uri	http://hdl.handle.net/10900/121666
dc.language.iso	en
dc.relation.uri	https://doi.org/10.1007/978-3-030-61609-0_12	de_DE
dc.title	Investigating efficient learning and compostionality in generative LSTM networks	de_DE
dc.type	BookPart	de_DE
utue.personen.pnd	Fabi, Sarah/514761857	de_DE
utue.personen.pnd	Otte, Sebastian/496372599	de_DE
utue.personen.pnd	Butz, Martin V./484796623	de_DE
utue.publikation.seiten	143-154	de_DE
utue.personen.roh	Fabi, Sarah	de_DE
utue.personen.roh	Otte, Sebastian	de_DE
utue.personen.roh	Wiese, Jonas Gregor	de_DE
utue.personen.roh	Butz, Martin V.	de_DE
utue.publikation.buchdesbeitrags	Artificial Neural Networks and Machine Learning – ICANN 2020 - 1st ed. 2020. - Cham : Springer International Publishing, 2020	de_DE
utue.titel.verfasserangabe	Sarah Fabi, Sebastian Otte, Jonas Gregor Wiese, and Martin V. Butz (Neuro-Cognitive Modeling Group, Eberhard Karls University Tübingen, Tübingen, Germany)	de_DE
utue.publikation.abrufzeichen	of07	de_DE
utue.publikation.swbdatum	2111	de_DE
utue.artikel.ppn	1777110858	de_DE