2023W

• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR
• Seminar in Artificial Intelligence: Machine Learning / 192.024 / SE

2024S

• Applied Deep Learning / 192.032 / VU
• Seminar for PhD Students / 192.031 / SE
• Seminar in Artificial Intelligence: Neurosymbolic Artificial Intelligence / 192.024 / SE

• To TTP or not to TTP?: Exploiting TTPs to Improve ML-based Malware Detection / Sharma, Y., Giunchiglia, E., Birnbach, S., & Martinovic, I. (2023). To TTP or not to TTP?: Exploiting TTPs to Improve ML-based Malware Detection. In Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience (CSR) (pp. 8–15). https://doi.org/10.1109/CSR57506.2023.10225000
• ROAD-R: the autonomous driving dataset with logical requirements / Giunchiglia, E., Stoian, M. C., Khan, S., Cuzzolin, F., & Lukasiewicz, T. (2023). ROAD-R: the autonomous driving dataset with logical requirements. Machine Learning, 112, 3261–3291. https://doi.org/10.1007/s10994-023-06322-z
• Exploiting T-norms for Deep Learning in Autonomous Driving / Stoian, M. C., Giunchiglia, E., & Lukasiewicz, T. (2023). Exploiting T-norms for Deep Learning in Autonomous Driving. In A. S. d’Avila Garcez, T. R. Besold, M. Gori, & E. Jimenez-Ruiz (Eds.), Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023) (pp. 369–380).
• Deep Learning with Logical Constraints / Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022). Deep Learning with Logical Constraints. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 5478–5485). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/767