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Deep Topology Learning (DeToL)

Deep Learning, i.e. deep neural networks (DNN), have become a key technology in recent years. However, the design of new, problem specific network topologies is still a time and compute intensive process. So far, the design of deep learning solutions for specific applications mostly follows a purely heuristic try and error process based on human expert knowledge and experience. Every network topology needs to be built from a large number of layer types and their configuration. Most layers themselves, as well as the employed training methods, have complex parameter spaces (so-called hyperparameters), whose impact on the final DNN performance is as large as the impact of the network topology itself.

In this project, we aim at facilitating a more efficient topology design process, rendering DNNs accessible to unexperienced users.

DeToL is funded by BMBF. Runtime: October 2018 - September 2021.

Partners

https://www.itwm.fraunhofer.de/de/abteilungen/hpc/Daten-Analyse-Maschinelles-Lernen.html

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info@detol.de

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