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2020 – today
- 2023
[c4]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. ICML 2023: 37624-37634- [c3]Dongxia Wu
, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Deep Bayesian Active Learning for Accelerating Stochastic Simulation. KDD 2023: 2559-2569 - [i7]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. CoRR abs/2305.04392 (2023) - 2022
- [j5]Nicolò Gozzi, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Marco Ajelli, Nicola Perra, Alessandro Vespignani:
Anatomy of the first six months of COVID-19 vaccination campaign in Italy. PLoS Comput. Biol. 18(5) (2022) - [c2]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. KDD 2022: 2029-2038 - [i6]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. CoRR abs/2206.04872 (2022) - 2021
- [j4]Jessica T. Davis, Matteo Chinazzi, Nicola Perra, Kunpeng Mu, Ana L. Pastore y Piontti, Marco Ajelli, Natalie E. Dean, Corrado Gioannini, Maria Litvinova, Stefano Merler, Luca Rossi, Kaiyuan Sun, Xinyue Xiong, Ira M. Longini Jr., M. Elizabeth Halloran, Cécile Viboud, Alessandro Vespignani:
Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nat. 600(7887): 127-132 (2021) - [j3]Fred S. Lu, André T. Nguyen, Nicholas B. Link, Mathieu Molina, Jessica T. Davis, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Marc Lipsitch, Mauricio Santillana:
Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches. PLoS Comput. Biol. 17(6) (2021) - [c1]Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. KDD 2021: 1841-1851 - [i5]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting. CoRR abs/2102.06684 (2021) - [i4]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. CoRR abs/2105.11982 (2021) - [i3]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Accelerating Stochastic Simulation with Interactive Neural Processes. CoRR abs/2106.02770 (2021) - 2020
- [i2]Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica T. Davis, Alessandro Vespignani, Mauricio Santillana:
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. CoRR abs/2004.04019 (2020) - [i1]Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu:
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks. CoRR abs/2006.11913 (2020)
2010 – 2019
- 2019
- [j2]Matteo Chinazzi, Bruno Gonçalves, Qian Zhang, Alessandro Vespignani:
Mapping the physics research space: a machine learning approach. EPJ Data Sci. 8(1): 33:1-33:18 (2019) - 2010
- [j1]Fabrizio Iozzi, Francesco Trusiano, Matteo Chinazzi, Francesco C. Billari, Emilio Zagheni, Stefano Merler, Marco Ajelli, Emanuele Del Fava, Piero Manfredi:
Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data. PLoS Comput. Biol. 6(12) (2010)
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