Daniele Gammelli
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Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis
R Seccia, D Gammelli, F Dominici, S Romano, AC Landi, M Salvetti, ...
PloS one 15 (3), e0230219, 2020
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
D Gammelli, I Peled, F Rodrigues, D Pacino, HA Kurtaran, FC Pereira
Transportation Research Part C: Emerging Technologies 120, 2020
Generalized Multi-Output Gaussian Process Censored Regression
D Gammelli, K Pryds Rolsted, D Pacino, R Filipe
arXiv preprint arXiv:2009.04822, 2020
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
D Gammelli, K Yang, J Harrison, F Rodrigues, FC Pereira, M Pavone
arXiv preprint arXiv:2104.11434, 2021
Recurrent Flow Networks: A Recurrent Latent Variable Model for Spatio-Temporal Density Modelling
D Gammelli, F Rodrigues
arXiv preprint arXiv:2006.05256, 2020
Modeling Transport Data Under Stochastic and Latent Censorship
I Peled, D Gammelli, F Rodrigues, D Pacino, FC Pereira
A Machine Learning Approach to Censored Bike-Sharing Demand Modeling
D Gammelli, F Rodrigues, D Pacino, HA Kurtaran, FC Pereira
Transportation Research Board. Annual Meeting Proceedings 2020, 2020
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Artículos 1–7