Frontiers

Graph-Guided Neural Networks: Revolutionizing Anomaly Detection in Chemical Engineering

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...
IEEE

Inconsistency-Aware Graph Convolutional Networks for Multi-View Classification

Abstract: Graph convolutional networks (GCNs) have attracted significant attention in the field of multi-view learning, as they effectively extract intricate information from diverse features.
Scientific Research Publishing

KAN-Transformer Fusion with Mixture of Experts for Temporal Imputation of Spatiotemporal Air Pollution Data ()

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
IEEE

GGCT-Net: A Dual-Branch Gated Graph Convolution and Grouped Cross-Attention Transformer Network for Hyperspectral Unmixing

Abstract: In recent years, deep learning-based hyperspectral unmixing (HU) techniques have garnered increasing attention and achieved significant progress. However, existing deep learning methods ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
C&EN

Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...
GitHub

Pull requests: Gabriele-Immordino/Spatio-temporal-Graph-Convolutional-Autoencoder-for-Transonic-Wing-Pressure-Distribution-Forecasting

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.