Jörn Hees

Machine learning models can reduce path loss prediction errors in LPWAN by over 30% compared to traditional methods, revolutionizing network planning for smart cities.

Continual learning for anomaly detection can now effectively handle heterogeneous data without succumbing to catastrophic forgetting or performance drops.