Ghent University Academic Bibliography

Advanced

Oger: modular learning architectures for large-scale sequential processing

David Verstraeten UGent, Benjamin Schrauwen UGent, Sander Dieleman UGent, Philémon Brakel UGent, Pieter Buteneers UGent and Dejan Pecevski (2012) JOURNAL OF MACHINE LEARNING RESEARCH. 13. p.2995-2998
abstract
Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large data sets. It builds on MDP for its modularity, and adds processing of sequential data sets, gradient descent training, several cross-validation schemes and parallel parameter optimization methods. Additionally, several learning algorithms are implemented, such as different reservoir implementations (both sigmoid and spiking), ridge regression, conditional restricted Boltzmann machine (CRBM) and others, including GPU accelerated versions. Oger is released under the GNU LGPL, and is available from http://organic.elis.ugent.be/oger.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
modular architectures, sequential processing, Python
journal title
JOURNAL OF MACHINE LEARNING RESEARCH
volume
13
pages
2995 - 2998
Web of Science type
Article
Web of Science id
000313200000006
JCR category
AUTOMATION & CONTROL SYSTEMS
JCR impact factor
3.42 (2012)
JCR rank
2/58 (2012)
JCR quartile
1 (2012)
ISSN
1532-4435
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3054005
handle
http://hdl.handle.net/1854/LU-3054005
alternative location
http://jmlr.csail.mit.edu/papers/volume13/verstraeten12a/verstraeten12a.pdf
date created
2012-11-16 16:14:44
date last changed
2013-07-04 14:36:03
@article{3054005,
  abstract     = {Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large data sets. It builds on MDP for its modularity, and adds processing of sequential data sets, gradient descent training, several cross-validation schemes and parallel parameter optimization methods. Additionally, several learning algorithms are implemented, such as different reservoir implementations (both sigmoid and spiking), ridge regression, conditional restricted Boltzmann machine (CRBM) and others, including GPU accelerated versions. Oger is released under the GNU LGPL, and is available from http://organic.elis.ugent.be/oger.},
  author       = {Verstraeten, David and Schrauwen, Benjamin and Dieleman, Sander and Brakel, Phil{\'e}mon and Buteneers, Pieter and Pecevski, Dejan},
  issn         = {1532-4435},
  journal      = {JOURNAL OF MACHINE LEARNING RESEARCH},
  keyword      = {modular architectures,sequential processing,Python},
  language     = {eng},
  pages        = {2995--2998},
  title        = {Oger: modular learning architectures for large-scale sequential processing},
  url          = {http://jmlr.csail.mit.edu/papers/volume13/verstraeten12a/verstraeten12a.pdf},
  volume       = {13},
  year         = {2012},
}

Chicago
Verstraeten, David, Benjamin Schrauwen, Sander Dieleman, Philémon Brakel, Pieter Buteneers, and Dejan Pecevski. 2012. “Oger: Modular Learning Architectures for Large-scale Sequential Processing.” Journal of Machine Learning Research 13: 2995–2998.
APA
Verstraeten, D., Schrauwen, B., Dieleman, S., Brakel, P., Buteneers, P., & Pecevski, D. (2012). Oger: modular learning architectures for large-scale sequential processing. JOURNAL OF MACHINE LEARNING RESEARCH, 13, 2995–2998.
Vancouver
1.
Verstraeten D, Schrauwen B, Dieleman S, Brakel P, Buteneers P, Pecevski D. Oger: modular learning architectures for large-scale sequential processing. JOURNAL OF MACHINE LEARNING RESEARCH. 2012;13:2995–8.
MLA
Verstraeten, David, Benjamin Schrauwen, Sander Dieleman, et al. “Oger: Modular Learning Architectures for Large-scale Sequential Processing.” JOURNAL OF MACHINE LEARNING RESEARCH 13 (2012): 2995–2998. Print.