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Showing 1 to 4 of 4 for “"Temporal Graph Neural Networks"”.
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A framework for programming and optimizing temporal graph neural networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that …
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Learning Spatio-Temporal Correlations from Dynamic and Sparse Data
The amount of spatio-temporal measurements around Earth, its atmosphere, in human-made urban settings, and in its oceans increases with affordable and smaller sensor technologies. Improved communication technologies allow sensors to be not geostationary, changing their positions and measurement …
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Machine Learning Driven Source Identification, State Estimation and Sensor Optimization in Water Systems
… by introducing a novel Multi-Layer Perceptron Neural Network (MLP-NN) surrogate model that effectively emulates the physics-based EPASWMM, enabling computationally efficient water quality simulations. This MLP-NN model is then integrated within a Genetic Algorithm (GA) optimization framework to …