Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

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 ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...