Martin Tappler
Projektass. Dr.techn.
Role
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PostDoc Researcher
Cyber-Physical Systems, E191-01
Publications
- Hierarchical Learning of Generative Automaton Models from Sequential Data / von Berg, B., Aichernig, B. K., Rindler, M., Štern, D., & Tappler, M. (2024). Hierarchical Learning of Generative Automaton Models from Sequential Data. In Software Engineering and Formal Methods (pp. 215–233). https://doi.org/10.1007/978-3-031-77382-2_13
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Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning
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Pranger, S., Chockler, H., Tappler, M., & Könighofer, B. (2024). Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning. In A. Globerson, L. Mackey, & D. Belgrave (Eds.), Advances in Neural Information Processing Systems 37 (NeurIPS 2024). http://hdl.handle.net/20.500.12708/213273
Project: TAIGER (2023–2027) -
On the Relationship Between RNN Hidden-State Vectors and Semantic Structures
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Muskardin, E., Tappler, M., Pill, I., Aichernig, B., & Pock, T. (2024). On the Relationship Between RNN Hidden-State Vectors and Semantic Structures. In Findings of the Association for Computational Linguistics ACL 2024 (pp. 5641–5658). https://doi.org/10.18653/v1/2024.findings-acl.335
Project: TAIGER (2023–2027)