Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback

Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	A. Emin Orhan, Wei Ji Ma
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9eb0080d125246cb88d6ac22181477e7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9eb0080d125246cb88d6ac22181477e7
record_format	dspace
spelling	oai:doaj.org-article:9eb0080d125246cb88d6ac22181477e72021-12-02T14:16:55ZEfficient probabilistic inference in generic neural networks trained with non-probabilistic feedback10.1038/s41467-017-00181-82041-1723https://doaj.org/article/9eb0080d125246cb88d6ac22181477e72017-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-00181-8https://doaj.org/toc/2041-1723Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.A. Emin OrhanWei Ji MaNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-14 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q A. Emin Orhan Wei Ji Ma Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
description	Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.
format	article
author	A. Emin Orhan Wei Ji Ma
author_facet	A. Emin Orhan Wei Ji Ma
author_sort	A. Emin Orhan
title	Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
title_short	Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
title_full	Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
title_fullStr	Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
title_full_unstemmed	Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
title_sort	efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9eb0080d125246cb88d6ac22181477e7
work_keys_str_mv	AT aeminorhan efficientprobabilisticinferenceingenericneuralnetworkstrainedwithnonprobabilisticfeedback AT weijima efficientprobabilisticinferenceingenericneuralnetworkstrainedwithnonprobabilisticfeedback
_version_	1718391683553427456

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback

Ejemplares similares