Tag: Graph Neural Networks

A new editorial in Frontiers in Control Engineering surveys how game theory, graph neural networks, and heterogeneous robot teams are ...

A new autonomous AI framework called DriftGuard detects, diagnoses, and corrects calibration drift in ocean sensor networks up to three ...

A new review in Frontiers in Bioinformatics surveys how machine learning and deep learning models are reshaping toxicity prediction across ...

Researchers in China developed an improved crystal graph neural network that screened more than 46,000 piezoelectric crystals and identified several ...

Researchers outline a comprehensive blueprint for building trustworthy foundation models that emulate expensive physics simulations on exascale supercomputers.

A new framework called ADTE combines graph-attentive neural networks, non-stationary reinforcement learning, and continual learning to deliver adversarially robust, certified ...

A new systematic review and meta-analysis of 23 studies finds that machine learning models for peptide–target interaction prediction achieve strong ...

A hybrid graph neural network and deep unrolling architecture achieves 98 percent success in super-resolution direction-of-arrival estimation while remaining robust ...

A topology-aware deep reinforcement learning framework that encodes drainage networks as graphs cut simulated flood overflow by over 31 percent ...

A new study shows that deep learning models can accurately predict the mechanical strength of biodegradable magnesium alloys while clearly ...