ZOPPIS ITALO FRANCESCO

Zancanaro, A., Cisotto, G., Zoppis, I., Manzoni, S. (2024). vEEGNet: Learning Latent Representations to Reconstruct EEG Raw Data via Variational Autoencoders. In M. Ziefle, M.D. Lozano, M. Mulvenna (a cura di), Information and Communication Technologies for Ageing Well and e-Health
9th International Conference, ICT4AWE 2023, Prague, Czech Republic, April 22–24, 2023, Revised Selected Papers (pp. 114-129). Springer [10.1007/978-3-031-62753-8_7]. Detail

Lazzarinetti, G., Dondi, R., Manzoni, S., Zoppis, I. (2024). An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks. ALGORITHMS, 17(2) [10.3390/a17020072]. Detail

Matamoros Aragon, R., Zoppis, I., Manzoni, S. (2023). When Attention Turn To Be Explanation. A Case Study in Recommender Systems. In xAI-2023:LB-D-DC - xAI-2023 Late-breaking Work, Demos and Doctoral Consortium Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023 (pp.129-134). Aachen : CEUR-WS. Detail

Zancanaro, A., Zoppis, I., Manzoni, S., Cisotto, G. (2023). vEEGNet: A New Deep Learning Model to Classify and Generate EEG. In Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2023, Prague, Czech Republic, April 22-24, 2023 (pp.245-252). Setúbal : Science and Technology Publications [10.5220/0011990800003476]. Detail

Cisotto, G., Zancanaro, A., Zoppis, I., Manzoni, S. (2023). hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [Altro]. Detail