Google Research

The Need for Open Source Software in Machine Learning

  • Soren Sonnenburg
  • Mikio L. Braun
  • Cheng Soon Ong
  • Samy Bengio
  • Leon Bottou
  • Geoff Holmes
  • Yann LeCun
  • Klaus-Robert Mueller
  • Fernando Pereira
  • Carl-Edward Rasmussen
  • Gunnar Raetsch
  • Bernhard Schoelkopf
  • Alexander Smola
  • Pascal Vincent
  • Jason Weston
  • Robert C. Williamson
Journal of Machine Learning Research, vol. 8 (2007), pp. 2443-2466

Abstract

Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not utilized, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.

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