Abstract: Graph contrastive learning (GCL) leverages semantic consistency as contrastive signals and has shown strong performance in semi-supervised node classification. However, real-world graphs ...

Abstract: This paper presents a novel semi-supervised multi-scale graph convolution autoencoder network for industrial fault diagnosis. The proposed model aims to address the challenge of limited ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...