Scientific publications

da Silva Barros, T., Giroire, F., Aparicio-Pardo, R., Perennes, S., Natale, E., Na-, E., & Pérennes, S. (2024). Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression. https://doi.org/10.1109/CCGRID59990.2024.00045

da Silva Barros, T., Ferre, D., Giroire, F., Aparicio-Pardo, R., Perennes, S., Ferré, D., Giroire, F., & Pérennes, S. (2024). Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget. Proceedings of the 53rd International Conference on Parallel Processing, 32, 961–970. https://doi.org/10.1145/3673038.3673106

Hu, J. C., Cavicchioli, R., Berardinelli, G., & Capotondi, A. (2024). ShareBERT: Embeddings Are Capable of Learning Hidden Layers. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 18225–18233. https://doi.org/10.1609/AAAI.V38I16.29781

Kaplan, C. G., Xu, C., Marfoq, O., Neglia, G., & Santana De Oliveira, A. (2024). A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses. Proceedings on Privacy Enhancing Technologies, 2024(1), 525–548. https://petsymposium.org/popets/2024/popets-2024-0031.php

Le Bars, B., Bellet, A., Tommasi, M., Scaman, K., & Neglia, G. (2024). Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm. International Conference on Machine Learning (ICML). https://hal.science/hal-04611418

Schild, L., Abidin, A., & Preneel, B. (n.d.). Fast Transciphering Via Batched And Reconfigurable LUT Evaluation. IACR Transactions on Cryptographic Hardware and Embedded Systems 2024(4). 

Cioflan, C., Cavigelli, L., Rusci, M., de Prado, M., & Benini, L. (2024). On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems. https://arxiv.org/abs/2403.10549v1

Daliparthi, V. S. S. A., Momen, N., Tutschku, K., & De Prado, M. (2023, September). ViSDM 1.0: Vision Sovereignty Data Marketplace a Decentralized Platform for Crowdsourcing Data Collection and Trading. In Proceedings of the 2023 ACM Conference on Information Technology for Social Good (pp. 374-383). https://scholar.google.com/citations?view_op=view_citation&hl=en&user=z5sQ1nYAAAAJ&sortby=pubdate&citation_for_view=z5sQ1nYAAAAJ:_FxGoFyzp5QC.

Daliparthi, V. S. S. A., Momen, N., Tutschku, K., & De Prado, M. (2023b). ViSDM: A Liquid Democracy based Visual Data Marketplace for Sovereign Crowdsourcing Data Collection. ACM International Conference Proceeding Series, 108–115. https://doi.org/10.1145/3590777.3590794

de Prado, M., Rusci, M., Donze, R., Capotondi, A., Monnerat, S., Benini, L., & Pazos, N. (2021). Robustifying the deployment of tinyML models for autonomous mini-vehicles. Proceedings - IEEE International Symposium on Circuits and Systems, 2021-May. https://doi.org/10.3390/S21041339

Ecker, W., Houdeau, D. et al. (2024): Edge AI: KI nahe am Endgerät. Technologie für mehr Datenschutz, Energieeffizienz und Anwendungen in Echtzeit. Whitepaper aus der Plattform Lernende Systeme, München. https://www.plattform-lernende-systeme.de/publikationen.html

Francobaldi, M., De Filippo, A., Borghesi, A., Pizurica, N., Jovancevi, I., Llewellynn, T., & de Prado, M. (2023). TinderAI: Support System for Matching AI Algorithms and Embedded Devices. The International FLAIRS Conference Proceedings, 36. https://doi.org/10.32473/FLAIRS.36.133100

Markaki, O., Papapostolou, A., Mouzakitis, S., Zrazinska, I., Sobek, U., Wilczek, T., Troumpoukis, A., Ziouvelou, X., Karkaletsis, V., Carrasco, A., Garcia, M., Roger, G., Micheli, A., Codagnone, J. A., De Prado, M., & O’Neill, S. (2023). Encouraging AI Adoption by SMEs: Opportunities and Contributions by the ICT49 Project Cluster. 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. https://doi.org/10.1109/IISA59645.2023.10345867