DA, a post-selection inference framework that provides valid p-values and false positive rate control for semi-supervised ...

Anomaly detection is the process of identifying data points, entities or events that fall outside the normal range. An anomaly is anything that deviates from what is standard or expected. Humans and ...

Explore how Lokeshkumar Madabathula combines Databricks, real-time anomaly detection, lakehouse architecture, and governed Agentic AI for data reliability.

Real-time precise point positioning (RT-PPP) depends on accurate satellite clock products, but phase jumps, frequency jumps and complex clock anomalies can destabilize network clock estimation. This ...

A new framework discretizes time-series data and splits it between global rule-based and Transformer-based local detectors, ...

Anomaly detection is the process of identifying events or patterns that differ from expected behavior. Anomaly detection can range from simple outlier detection to complex machine learning algorithms ...

Training-Free Few-Shot Industrial Anomaly Detection via Decision-Guided Refinement (VerdictAD). - sgstq0811/VerdictAD-Jev-AD ...

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 ...