Abstract: Variational graph auto-encoders (VGAEs) are a key tool for node clustering, but existing models face several significant challenges. These challenges include a mismatch between inference and ...

GNODEVAE is a computational framework that integrates Graph Attention Networks (GAT), Neural Ordinary Differential Equations (NODE), and Variational Autoencoders (VAE). It targets three common ...

Abstract: Addressing the challenge of potential multiple and complex correlations between fault cases that are difficult to discern and thus fail to effectively guide similar fault handling, this ...

Single-cell multiomics sequencing techniques have rapidly developed in the past few years. Among these techniques, single-cell cellular indexing of transcriptomes and epitopes (CITE-seq) allows ...

Deep learning algorithms' powerful capabilities for extracting useful latent information give them the potential to outperform traditional financial models in solving problems of the stock market ...

Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized ...