Tag: Graph Neural Networks

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 ...

A new framework called VR-FraudNet forces large language models to propose structured fraud explanations that a fixed deterministic verifier must ...

A new graph-based machine learning method called GraphiRNA models the relationships among microRNAs, including negative correlations, to diagnose Alzheimer's disease ...

A new deep learning framework called MedSegNet-AXU segments multiple sclerosis lesions from 3D MRI with a 98.58 percent Dice score ...

Researchers at Fuzhou University have developed RCGELM-AE, a graph embedding model that trains in closed form without gradient descent while ...

Researchers have developed SURGE, a graph unlearning framework that treats data deletion requests as structural perturbations and repairs graph neural ...

Researchers in China have developed an interpretable multimodal AI framework that predicts the research octane number of pure compounds and ...

A systematic review in Applied Intelligence introduces a two-axis taxonomy that maps how large language models enhance graph neural networks ...