Tag: Graph Neural Networks

Researchers have developed a graph-enhanced bidirectional GRU model that detected 44 of 45 simulated anomalies in a realistic power generation ...

A comprehensive review of 53 studies shows deep learning systems can automatically segment lymphoma lesions on PET/CT, non-invasively classify disease, ...

Researchers have developed CSCProv, a compression framework that shrinks massive provenance graphs by up to thirty times while preserving the ...

Researchers have developed GRAB-FL, a graph-aware federated learning framework that uses graph neural networks to assign trust scores to client ...

Researchers have developed a physics-aware graph neural network that detects power grid anomalies hidden by manipulated measurements and deceptive network ...

A new causal reasoning framework called CCMRD uses counterfactual textual interventions, dot-product visual causal intervention, and graph neural networks to ...

Researchers have proven that minimizing graph counterfactual explanations is NP-hard and introduced Local Bounded Search, an algorithm that shrinks graph-based ...

A new survey in Vicinagearth provides the first systematic review of multi-intent spoken language understanding, comparing decoding strategies, modeling approaches, ...

Researchers have unveiled a data-level prompt injection attack that hijacks graph prompt learning systems by injecting a tiny fraction of ...

A new framework called TDiMS rebuilds molecular descriptors around pairs of chemically meaningful substructures, aiming to make predictions of intramolecular ...