Compartir
Titel
Neural PCA and Maximum Likelihood Hebbian Learning on the GPU
Autor(es)
Schlagwort
Computer Science
Fecha de publicación
2012
Verlag
Springer Science + Business Media
Citación
Artificial Neural Networks and Machine Learning – ICANN 2012 Lecture Notes in Computer Science. 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II. Lecture Notes in Computer Science. Volumen 7553, pp. 132-139.
Resumen
This study introduces a novel fine-grained parallel implementation of a neural principal component analysis (neural PCA) variant and the maximum Likelihood Hebbian Learning (MLHL) network designed for modern many-core graphics processing units (GPUs). The parallel implementation as well as the computational experiments conducted in order to evaluate the speedup achieved by the GPU are presented and discussed. The evaluation was done on a well-known artificial data set, the 2D bars data set.
URI
ISBN
978-3-642-33265-4 (Print) / 978-3-642-33266-1 (Online)
ISSN
0302-9743 (Print) / 1611-3349 (Online)
Aparece en las colecciones
- BISITE. Congresos [232]
Dateien zu dieser Ressource
Tamaño:
406.4Kb
Formato:
Adobe PDF