Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference. - Artur Mrowca; Martin Nocker; Sebastian Steinhorst; Stephan Günnemann (DAC, 2019)

Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning. - Thomas Widmann; Florian Merkle; Martin Nocker; Pascal Schöttle (EANN, 2023)

Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop. - Florian Merkle; Mihaela Roxana Sirbu; Martin Nocker; Pascal Schöttle (ICIAP Workshops (1), 2023)

HE-MAN - Homomorphically Encrypted MAchine learning with oNnx models. - Martin Nocker; David Drexel; Michael Rader; Alessio Montuoro; Pascal Schöttle (ICMLT, 2023)

On the Effect of Adversarial Training Against Invariance-based Adversarial Examples. - Roland Rauter; Martin Nocker; Florian Merkle; Pascal Schöttle (ICMLT, 2023)

Incremental Whole Plate ALPR Under Data Availability Constraints. - Markus Russold; Martin Nocker; Pascal Schöttle (ICPRAM, 2024)

Navigating the Trade-Off Between Explainability and Privacy. - Johanna Schmidt; Verena Pietsch; Martin Nocker; Michael Rader; Alessio Montuoro (VISIGRAPP (1): GRAPP, HUCAPP, IVAPP, 2024)

FHE ML Tuxedo: A Tailored Wrapper Architecture for Homomorphic Encryption in Machine Learning. - Martin Nocker; Linus Henke; Pascal Schöttle (DSN-W, 2025)

Less Is More: The Influence of Pruning on the Explainability of CNNs. - Florian Merkle; David Weber; Pascal Schöttle; Stephan Schlögl; Martin Nocker (IEEE Access, 2025)

Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices - A Roadmap. - Pascal Schöttle; Matthias Janetschek; Florian Merkle; Martin Nocker; Christoph Egger 003 (CoRR, 2025)