Learning Feature Representations with a Cost-Relevant Sparse Autoencoder (English)
- New search for: Längkvist, Martin
- New search for: Längkvist, Martin
- New search for: Loutfi, Amy
In:
International journal of neural systems
;
25
, 1
;
2015
-
ISSN:
- Article (Journal) / Print
-
Title:Learning Feature Representations with a Cost-Relevant Sparse Autoencoder
-
Contributors:Längkvist, Martin ( author ) / Loutfi, Amy
-
Published in:
-
Publisher:
- New search for: World Scientific
-
Place of publication:Singapore [u.a.]
-
Publication date:2015
-
ISSN:
-
ZDBID:
-
DOI:
-
Type of media:Article (Journal)
-
Type of material:Print
-
Language:English
-
Keywords:
-
Classification:
-
Source:
Table of contents – Volume 25, Issue 1
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
-
Exploring a Type of Central Pattern Generator Based on Hindmarsh–Rose Model: From Theory to ApplicationZhang, Dingguo et al. | 2015
-
Adaptive Control of Parkinson's State Based on a Nonlinear Computational Model with Unknown ParametersSu, Fei et al. | 2015
-
Learning Feature Representations with a Cost-Relevant Sparse AutoencoderLängkvist, Martin et al. | 2015
-
A Predictive Modeling Approach to Analyze Data in EEG–fMRI ExperimentsFerdowsi, Saideh et al. | 2015
-
Acoustic Space Learning for Sound-Source Separation and Localization on Binaural ManifoldsDeleforge, Antoine et al. | 2015
-
Spreading codes enables the blind estimation of the hemodynamic response with short-events sequencesGerven, M. van et al. | 2015