Abstract: Recently, autoencoder (AE)-based hyperspectral anomaly detection methods have demonstrated excellent performance on hyperspectral images (HSIs). The AE can simultaneously reconstruct both ...

This demo highlights how one can use a semi-supervised machine learning technique based on autoencoder to detect an anomaly in sensor data (output pressure of a triplex pump). The demo also shows how ...

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 ...

Abstract: Anomaly detection is critical given the raft of cyber attacks in the wireless communications these days. It is thus a challenging task to determine network anomaly more accurately. In this ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

MST-VAE is an unsupervised learning approach for anomaly detection in multivariate time series. Inspired by InterFusion paper, we propose a simple yet effective multi-scale convolution kernels applied ...

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...