CORDIS Project
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This project focuses on enhancing graph neural networks by developing trustworthy pooling layers that improve computational efficiency and predictive performance. It integrates concepts from signal processing and random methods to create a robust, open-source toolbox for various applications.
Graph neural networks (GNNs) are deep learning architectures that hold the promise of durably changing our way of, e.g., predicting inter-molecular chemical affinity to accelerate drug discovery or smartly balancing power loads on the electricity grid.
My project focuses on pooling layers, a major building block of neural networks, including GNNs.
In a nutshell, pooling layers generate local summaries of the data to enable faster computations and generate discriminative multiscale representation…
UNIVERSITETET I TROMSOE - NORGES ARKTISKE UNIVERSITET
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