VIZZARI GIUSEPPE

Vizzari, G., Briola, D., Cecconello, T. (2023). Curriculum–Based Reinforcement Learning for Pedestrian Simulation: Towards an Explainable Training Process. In Proceedings of the 24th Workshop "From Objects to Agents" (pp.32-48). CEUR-WS. Detail

Chimisso, R., Buršić, S., Marocco, P., Vizzari, G., Ognibene, D. (2023). Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Pictures. Intervento presentato a: The Algonauts Project 2023 - Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Pictures Special session at 2023 Conference on Cognitive Computational Neuroscience, Oxford, UK. Detail

Bordiu, C., Bufano, F., Cecconello, T., Sciacca, E., Riggi, S., Vizzari, G. (2023). Patterns in the Chaos: An Unsupervised View of Galactic Supernova Remnants. In Machine Learning for Astrophysics
Proceedings of the ML4Astro International Conference 30 May - 1 Jun 2022 (pp.61-65). Springer Science and Business Media B.V. [10.1007/978-3-031-34167-0_13]. Detail

Feliciani, C., Jia, X., Murakami, H., Ohtsuka, K., Vizzari, G., Nishinari, K. (2023). Social groups in pedestrian crowds as physical and cognitive entities: Extent of modeling and motion prediction. TRANSPORTATION RESEARCH. PART A, POLICY AND PRACTICE, 176(October 2023) [10.1016/j.tra.2023.103820]. Detail

Pałka, D., Lubaś, R., Vizzari, G., Wąs, J. (2023). Comparison of the Use of UWB and BLE as Positioning Methods in Data-Driven Modeling of Pedestrian Dynamics. In Parallel Processing and Applied Mathematics
14th International Conference, PPAM 2022, Gdansk, Poland, September 11–14, 2022, Revised Selected Papers, Part II (pp.492-501). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-30445-3_41]. Detail