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Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights
This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the complexities of multi-task and multi-modal learning in digital health and longitudinal studies.
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spaces
This paper introduces a novel methodology for Feature Selection for Functional Classification, FSFC, that addresses the challenge of jointly performing feature selection and classification of functional data in scenarios with categorical responses and multivariate longitudinal features.
Enabling the Evaluation of Driver Physiology Via Vehicle Dynamics
Driving is a daily routine for many individuals across the globe.
