EurekAlert!

Real-time, large-scale graph neural network inference through BingoCGN

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 ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
EurekAlert!

Soft-GNN: towards robust graph neural networks via self-adaptive data utilization

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...
AlphaGalileo

Breakthrough in Efficient GNN Training Architecture: Research Team Proposes Hardware-Algorithm Co-Optimization Solution

A Chinese research team has achieved a breakthrough in improving the training efficiency of Graph Neural Networks (GNNs). They introduced an ...
IndianWeb2

Nvidia Acquires Kumo AI in ~$400M Push Into Enterprise Predictive Models

Nvidia acquires Kumo AI for $400M, boosting enterprise predictive models with graph neural networks and automation for global business data.
Visual Studio Magazine

Neural Network Quantile Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...