LeaRNNify - New Challenges for Recurrent Neural Networks and Grammatical Inference
The project LeaRNNify (https://www.learnnify.org) is at the interface of formal methods and artificial intelligence. Its aim is to bring together two different kinds of algorithmic learning, namely grammatical inference and learning of neural networks. More precisely, we promote the use of recurrent neural networks (RNNs) in the process of verifying reactive systems, which until now has been reserved for grammatical inference. On the other hand, grammatical inference is finding its way into the field of classical machine learning. In fact, our second goal is to use automata-learning techniques to enhance the verification, explainability, and interpretability of machine-learning algorithms and, in particular, RNNs.
The project is funded by the Procope programme of Campus France and the German Academic Exchange Service (DAAD).
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- Martin Leucker
- Diedrich Wolter
- Ulrike Schräger-Ahrens
- Aliyu Ali
- Mahmoud Abdelrehim
- Phillip Bende
- Juljan Bouchagiar
- Marc Bätje
- Tobias Braun
- Gerhard Buntrock
- Anja Grotrian
- Hannes Hesse
- Raik Hipler
- Elaheh Hosseinkhani
- Hannes Kallwies
- Frauke Kerlin
- Karam Kharraz
- Mohammad Khodaygani
- Ludwig Pechmann
- Waqas Rehan
- Martin Sachenbacher
- Andreas Schuldei
- Annette Stümpel
- Gesina Schwalbe
- Tobias Schwartz
- Daniel Thoma
- Lars Vosteen
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