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Found 5 papers, 3 papers with code
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
The Exa. TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking.
Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning.
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables.
Benchmarking GPU and TPU Performance with Graph Neural Networks
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models.
