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Random sampling for trustworthy pooling layers in graph neural networks

CORDIS · observation · Publication date unknown

Random sampling for trustworthy pooling layers in graph neural networks 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 representations. The traditional periodic-sampling-based pooling that has proven so successful for data defined on regular grids such as time series or images becomes ill-defined when the underlying space of the data loses its regularity such as data defined over graphs. Todays existing solutions stem from a trial-and-error approach often driven by two practical and somewhat short-sighted objectives: computation efficiency and empirical predi

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recordType

award

status

SIGNED

region

EU

value

226751.04

unit

EUR