SM-GNN prunes multi-view GNNs to pure propagation, cutting training time while outperforming prior MKGC accuracies on two ...

Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...

Abstract: Graph neural networks (GNNs) have shown promise in graph classification tasks, but they struggle to identify out-of-distribution (OOD) graphs often encountered in real-world scenarios, ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...

Positive predictive value was higher with Multicenter Epilepsy Lesion Detection Graph compared with existing baseline algorithm. HealthDay News — A graph neural network using data from the Multicenter ...