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|Abstract=Research on neural networks has gained significant momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.
 
|Abstract=Research on neural networks has gained significant momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.
 
|ISBN=1613-0073
 
|ISBN=1613-0073
|Download=FAIRnets_Search
+
|Download=FAIRnets_Search.pdf
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
 
}}
 
}}

Aktuelle Version vom 7. Oktober 2019, 10:44 Uhr


FAIRnets Search - A Prototype Search Service to Find Neural Networks




Published: 2019

Buchtitel: Poster&Demos at SEMANTICS 2019
Ausgabe: 2451
Verlag: CEUR Workshop Proceedings
Organisation: Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems

Referierte Veröffentlichung

BibTeX

Kurzfassung
Research on neural networks has gained significant momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.

ISBN: 1613-0073
Download: Media:FAIRnets_Search.pdf



Forschungsgruppe

Web Science


Forschungsgebiet