IEEE

Semi-Supervised Learning With Graph Learning-Convolutional Networks

Abstract: Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed ...

AI teams: The new blueprint for enterprise automation

1. Risk: AI Monoculture (Shared Blind Spots). This is the most critical and overlooked systemic vulnerability. Building your ...
IEEE

A Multi-Graph Attention Spatial-Temporal Graph Convolutional Network (MGA-STGCN) for AHP Risk Forecasting

Abstract: We propose a Multi-graph Attention spatial-temporal graph convolutional network (MGA-STGCN) for AHP risk forecasting. To describe the temporal and spatial features of the area, we use ...
GitHub

stabilizer-code

Clifford circuits, graph states, and other quantum Stabilizer formalism tools. An intuitive programming package for simulating and analyzing Clifford-dominated circuits, quantum measurement, and ...