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- “Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space“, Giacomo Verardo, Daniel F. Perez-Ramirez, Samuel Bruchfeld, Magnus Boman, Marco Chiesa, Sabine Koch, Gerald Q. Maguire Jr., and Dejan Kostić, Proceedings of the 15th Statistical Atlases and Computational Modeling of the Heart (STACOM), October 2024.
- “FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge“, Giacomo Verardo, Magnus Boman, Samuel Bruchfeld, Marco Chiesa, Sabine Koch, Gerald Q. Maguire Jr., and Dejan Kostic, Proceedings of the 4th International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI), July 2024.
- “Optimizing Neural Network Models for Healthcare and Federated Learning“, Giacomo Verardo’s Licentiate Thesis at KTH, May 2024.
- “Fast Server Learning Rate Tuning for Coded Federated Dropout“, Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic and Gerald Quentin Maguire Jr., In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 85-100. Springer, Cham, 2023
- “Fast Server Learning Rate Tuning for Coded Federated Dropout“, Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic and Gerald Quentin Maguire Jr., International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI), June 2022.
