Tag: Graph Neural Networks

Researchers have combined conditional diffusion models with graph neural networks to generate coordinated energy schedules for building clusters, achieving a ...

Researchers in India have built a hybrid graph neural network framework that converts retinal images into graph structures, achieving up ...

Researchers in India have published a detailed mathematical blueprint for a spatio-temporal graph foundation model designed to learn continuously from ...

A hybrid graph neural network that combines message passing with topological persistence embeddings shows improved robustness to edge removal on ...

A hybrid AI model that reasons about park spaces as interconnected graphs before generating images produces urban green space layouts ...

An explainable graph neural network called SpaCEy identifies the spatial tissue patterns and protein markers that predict cancer survival and ...

A new physics-embedded machine learning framework called SPEL-VFL reconstructs hidden hydrogen energy flows in smart grids and pipeline networks even ...

Researchers at ETH Zurich and Roche have combined active learning with geometric and self-supervised deep learning to predict C–H borylation ...

Researchers have developed SeqCube-HT, a framework that uses trace-backed state-cube graphs to generate verifiably executable sequential hardware Trojan benchmarks far ...

Researchers have developed CL3AN-GNN, a graph neural network that borrows curriculum learning from education to progressively disentangle features and boost ...