Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

A review from Xidian University shows that advanced computational algorithms—from neural networks and matrix methods to recommendation engines and ...

The Data Science Lab Data Dimensionality Reduction Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation ...

Keywords: microbe-disease associations, graph attention autoencoder, positive-unlabeled learning, K -means, XGBoost, deep neural network Citation: Peng L, Huang L, Tian G, Wu Y, Li G, Cao J, Wang P, ...

Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, ...

Machine learning on graphs is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is ...

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 ...

Generative model and example outputs. (A) Architectural schematic of the conditional-VAE-based relational graph-convolutional neural network, presented above illustrations of our activity conditioning ...

Graph neural networks are very powerful tools. They have already found powerful applications in domains such as route planning, fraud detection, network optimization, and drug research.