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The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge

 
: Heese, R.; Walczak, M.; Morand, L.; Helm, D.; Bortz, M.

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Fulltext ()

Tetko, I.V.:
Artificial Neural Networks and Machine Learning - ICANN 2019. Workshop and Special Sessions. Proceedings : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11731)
ISBN: 978-3-030-30492-8 (Print)
ISBN: 978-3-030-30493-5 (Online)
pp.391-395
International Conference on Artificial Neural Networks (ICANN) <28, 2019, Munich>
English
Conference Paper, Electronic Publication
Fraunhofer IWM ()
Fraunhofer ITWM ()
expert knowledge; mixture of experts; custom loss

Abstract
We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box artificial neural network (ANN) model with two different levels of expert knowledge and benchmark them against a pure black-box model.

: http://publica.fraunhofer.de/documents/N-559251.html