Making Neural Networks FAIR

« Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a recent trend to attempt to re-use already-trained neural networks. As such, neural networks themselves have become research data. In this paper, we present the Neural Network Ontology, an ontology to make neural networks findable, accessible, interoperable and reusable as suggested by the well-established FAIR guiding principles for scientific data management and stewardship. We created the new FAIRnets Dataset that comprises about 2,000 neural networks openly accessible on the internet and uses the Neural Network Ontology to semantically annotate and represent the neural networks. (…) »

source > arxiv.org, Anna Nguyen, Tobias Weller, York Sure-Vetter, 26 juillet 2019, arXiv:1907.11569v1

• Mots-clés : 
• Deep learning,
• FAIR,
• Intelligence artificielle (IA),
• Machine learning,
• Ontologie