The Need for Open Source Software in Machine Learning

By Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, L. Eon Bottou, G. Holmes, Yann LeCun, K. Müller, Fernando C Pereira, Carl E. Rasmussen and 6 more. 2007

244 citations recorded by the source corpus.

Subfields: ML Systems & Hardware, Social Sciences & Humanities

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Citations in the corpus

BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, methodology): “…and collected conversations with interested researchers in order to make this research accessible and reproducible, and thus to enable further research into responsible conversational AI (Sonnenburg et al., 2007; Pineau et al., 2021; Zhang et al., 2022; Roller et al., 2020; Dinan et al., 2021).”
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, methodology): “coding-algorithms-frankenalgos-program-danger Sonnenburg S et al. (2007) The need for open source software in machine learning. J Mach Learn Res 8:2443–2466 Supiot A (2017) Governance by numbers: the making of a legal model of allegiance. Hart Publishing, Oxford; Portland, Oregon Taleb NN (2007) The”
OpenML: networked science in machine learning (2014, background): “This lack of reproducibility has been warned against repeatedly [20; 38; 32], and has been highlighted as one of the most important challenges in data mining research [16].”
Replicability is not Reproducibility:Nor is it Good Science (2009, background): “(Sonnenburg et al., 2007, page 2449).”
Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “This calls for adequate machine learning usability [118].”
oTree - An Open-Source Platform for Laboratory, Online, and Field Experiments (2016, background): “Open source: Sonnenburg et al. (2007) have argued that in scientific applications, the source code should be available for inspection and available free of charge to enable replication and extension.”
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) (2020, background): “This includes making code, data, scientific communications and any other research artifact publicly available and easily accessible over the long-term, thereby increasing the transparency of the research process and improving the reporting quality in scientific manuscripts (Sonnenburg et al., 2007).”
Predictive, personalized, preventive, participatory (P4) cancer medicine (2011, methodology): “In addition, it will be necessary to 'version' the data and models in ways that are reminiscent of those approaches found to be highly efficient for building open access software”
The SHOGUN Machine Learning Toolbox (2010, background): “Furthermore, it provides the basic functionality for solving label sequence learning problems (Hidden Semi-Markov SVMs; cf. Rätsch and Sonnenburg, 2007; Schweikertet al., 2009).”
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, background): “to more efficient debugging and gives rise to accelerated scientific progress (Sonnenburg et al. 2007).”
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, background): “, 2017) and ML (Sonnenburg et al., 2007; Stodden et al., 2014; Vanschoren et al., 2014) communities to improve reproducibility.”
KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, background): “The main motivation for applying EAs to knowledge extraction tasks is that they are robust and adaptive search methods that perform a global search in place of candidate solutions (for instance, rules or other forms of knowledge representation).”
Support Vector Machines and Kernels for Computational Biology (2008, background): “org as part of an initiative advocating distribution of machine learning algorithms as open source software [74].”