It is trained to recognize the normal detector behavior from existing good data and to detect any deviations. The cornerstone of this approach is an autoencoder-based anomaly detection system.

In the project, we are addressing this task by leveraging several deep learning graph-based approaches, to detect anomalies within the Parallels Remote Application Server (RAS) user experience and ...

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years.

The Data Science Lab Data Anomaly Detection Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that ...