Stage-oe-small.jpg

Inproceedings123

Aus Aifbportal
Version vom 8. Januar 2021, 11:39 Uhr von Ns1888 (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Nguyen |ErsterAutorVorname=Anna }} {{Publikation Author |Rank=2 |Author=Tobias Weller }} {{Publikation Author |…“)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu:Navigation, Suche


Making Neural Networks FAIR


Making Neural Networks FAIR



Published: 2020 November
Herausgeber: Villazón-Terrazas et al.
Buchtitel: Knowledge Graphs and Semantic Web Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020, Mérida, Mexico, November 26–27, 2020, Proceedings
Nummer: 1232
Reihe: Communications in Computer and Information Science
Seiten: 29-44
Verlag: Springer

Referierte Veröffentlichung

BibTeX

Kurzfassung
Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles. Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks from GitHub based on this ontology, which we provide to the public as a knowledge graph called FAIRnets, ready to be used for recommending suitable neural networks to data scientists.

ISBN: 978-3-030-65383-5
Weitere Informationen unter: Link



Forschungsgruppe

Web Science


Forschungsgebiet

Maschinelles Lernen, Künstliche Intelligenz, Semantic Web