A novel architecture and training strategy for graph neural networks (GNN). The proposed architecture, named as Autoencoder-Aided GNN (AA-GNN), compresses the convolutional features at multiple hidden ...

In the context of these analyses, Graph Neural Networks (GNNs) emerge as powerful tools for considering the proximity of sample neighbors in anomaly detection and data classification, particularly ...

In recent years, graph-based deep learning algorithms have attracted widespread attention in the field of consumer electronics. Still, most of the current graph neural networks are based on supervised ...

Graph Autoencoder Networks. The wide application of autoencoder (AE) and its variants in the field of unsupervised learning has led to an increasing number of AE-based graph generation models. Sparse ...

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network Lihong Peng 1,2 † Liangliang Huang 1 † Geng Tian 3 Yan Wu 3 ...

An autoencoder is a neural network that learns to predict its input. After training, the demo scans through the 1,000 images and finds the one image which is most anomalous, ... Anomaly detection ...