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Found 12 papers, 7 papers with code
Social AI and the Challenges of the Human-AI Ecosystem
In order to understand the impact of AI on socio-technical systems and design next-generation AIs that team with humans to help overcome societal problems rather than exacerbate them, we propose to build the foundations of Social AI at the intersection of Complex Systems, Network Science and AI.
The unequal effects of the health-economy tradeoff during the COVID-19 pandemic
The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic.
Multi-fidelity Hierarchical Neural Processes
MF-HNP is flexible enough to handle non-nested high dimensional data at different fidelity levels with varying input and output dimensions.
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations.
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal forecasting.
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting.
Predicting seasonal influenza using supermarket retail records
In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
% We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters.
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
We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time.
Inferring high-resolution human mixing patterns for disease modeling
Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics.
Populations and Evolution Physics and Society Quantitative Methods
Detecting global bridges in networks
This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure.
Social and Information Networks Physics and Society
Extracting the multiscale backbone of complex weighted networks
Here we define a filtering method that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistical significant deviations with respect to a null model for the local assignment of weights to edges.
Physics and Society Disordered Systems and Neural Networks Networking and Internet Architecture
