Data Integration and Provisioning of Storage Systems for Data Mining in Production-process Data
REAPER: A Framework for Materializing and Reusing Deep-Learning Models
(Third Party Funds Group – Sub project)
Overall project: EFRE EIASY-Opt - Competence and Analysis Project for the "Data-driven Process and Production Optimization with the help of Data Mining and Big Data"
Project members: ,
Start date: 01/01/2017
End date: 31/12/2020
Acronym: E|ASY-Opt INF6
Funding source: Sonstige EU-Programme (z. B. RFCS, DG Health, IMI, Artemis), Bayerische Staatsministerien
Within the framework of the EFRE-E|ASY-Opt subproject, the potential of data mining methods in the area of manufacturing is being investigated. Especially the training of Deep-Learning models is a computationally intensive task, which may take hours or several days. The training time can be shortened considerably by using an already trained model, as long as the goal and source task are closely related. This connection is not yet fully understood.
The aim of this research project is to implement a system called REAPER (Reusable Neural Network Pattern Repository) to support data scientists in storing and reusing already trained deep learning models.
Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models
International Conference on Data Engineering (Macau SAR, China, 08/04/2019 - 11/04/2019)
- Job title: Holder of Chair
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- Cécile Astor
- Phone number: +49 9131 85-27893
- Fax number: +49 9131 85-28854
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- Office hours: Weekly Mon, 8:30 - 14:00, Room 08.139,
Weekly Tue, Wed, Thu, 8:30 - 13:30, Room 08.139,