Martin Nocker

According to our database, Martin Nocker authored at least 10 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices - A Roadmap.

Pascal Schöttle, Matthias Janetschek, Florian Merkle, Martin Nocker, Christoph Egger

CoRR, April, 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

FHE ML Tuxedo: A Tailored Wrapper Architecture for Homomorphic Encryption in Machine Learning.

Martin Nocker, Linus Henke, Pascal Schöttle

Proceedings of the 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2025

2024

Navigating the Trade-Off Between Explainability and Privacy.

Johanna Schmidt, Verena Pietsch, Martin Nocker, Michael Rader, Alessio Montuoro

Proceedings of the 19th International Joint Conference on Computer Vision, 2024

Incremental Whole Plate ALPR Under Data Availability Constraints.

Markus Russold, Martin Nocker, Pascal Schöttle

Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, 2024

2023

On the Effect of Adversarial Training Against Invariance-based Adversarial Examples.

Roland Rauter, Martin Nocker, Florian Merkle, Pascal Schöttle

Proceedings of the 8th International Conference on Machine Learning Technologies, 2023

HE-MAN - Homomorphically Encrypted MAchine learning with oNnx models.

Martin Nocker, David Drexel, Michael Rader, Alessio Montuoro, Pascal Schöttle

Proceedings of the 8th International Conference on Machine Learning Technologies, 2023

Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop.

Florian Merkle, Mihaela Roxana Sirbu, Martin Nocker, Pascal Schöttle

Proceedings of the Image Analysis and Processing - ICIAP 2023 Workshops, 2023

Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning.

Thomas Widmann, Florian Merkle, Martin Nocker, Pascal Schöttle

Proceedings of the Engineering Applications of Neural Networks, 2023

2019

Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference.

Artur Mrowca, Martin Nocker, Sebastian Steinhorst, Stephan Günnemann

Proceedings of the 56th Annual Design Automation Conference 2019, 2019