In a remarkable article in Journal of Machine Learning Research (JMLR), a group of authors advertise an open source model for peer-reviewed software publication in the scientific machine learning community. The authors argue that the lack of scientific recognition for good software motivates the development of algorithms rather than implementations and that this hinders the true potential of the learning algorithms.
The article is remarkable because of the content of course, but also because JMLR is normally a strictly technical journal and the authors are high-profiled within the community. The complete author list is:
- Soren Sonnenburg, FIRST
- Mikio L. Braun, TU Berlin
- Cheng Soon Ong, Friedrich Miescher Lab
- Samy Bengio, Google
- Leon Bottou, NEC Lab
- Geoffrey Holmes, Univ Waikato
- Yann LeCun, NY Univ
- Klaus-Robert Muller, TU Berlin
- Fernando Pereira, Univ Pennsylvania
- Carl Edward Rasmussen, Cambridge
- Gunnar Ratsch, Friedrich Miescher Lab
- Bernhard Scholkopf, Max Planck
- Alexander Smola, Australia National Univ
- Pascal Vincent, Univ Montreal
- Jason Weston, NEC Lab
- Robert C. Williamson, Australia National Univ
Read the article (it is open source/access): "The Need for Open Source Software in Machine Learning", Journal of Machine Learning Research 8 (2007) 2443-2466. (PDF)