VIZZARI GIUSEPPE

Piskovskyi, V., Chimisso, R., Patania, S., Foulsham, T., Vizzari, G., Ognibene, D. (2024). Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimuli. Intervento presentato a: Human-inspired Computer Vision - ECCV 2024 Workshop - 29th September 2024, Milan, Italy [10.48550/arXiv.2410.04497]. Detail

Briola, D., Tinti, F., Vizzari, G. (2024). Creating Virtual Reality Scenarios for Pedestrian Experiments Focusing on Social Interactions. In Proceedings of the 25th Workshop "From Objects to Agents" (pp.170-185). CEUR-WS. Detail

Bufano, F., Bordiu, C., Cecconello, T., Munari, M., Hopkins, A., Ingallinera, A., et al. (2024). Sifting the debris: Patterns in the SNR population with unsupervised ML methods. ASTRONOMY & ASTROPHYSICS, 690(October 2024), 1-17 [10.1051/0004-6361/202451096]. Detail

Masiero, E., Bursic, S., Trianni, V., Vizzari, G., Ognibene, D. (2024). In Search of Compositional Multi-Task Deep Architectures for Information Theoretic Field Exploration. In 20th IEEE International Conference on Automation Science and Engineering, CASE 2024 (pp.612-617). IEEE Computer Society [10.1109/CASE59546.2024.10711675]. Detail

Vizzari, G., Briola, D., Pisapia, F. (2024). Curriculum-Based RL for Pedestrian Simulation: Sensitivity Analysis and Hyperparameter Exploration. In Thirteenth International Workshop on Agents in Traffic and Transportation
co-located with the the 27th European Conference on Artificial Intelligence (ECAI 2024) (pp.136-149). CEUR-WS. Detail