5 papers
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
Alberto D. Cencillo, Leonardo Concepción, Julián Luengo +1
Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities. At the Spallation Neutron Source (SNS), the High Voltage Convert…
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Alberto D. Cencillo, Leonardo Concepción, Isaac Triguero +1
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the c…
Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting
Ignacio Aguilera-Martos, Andrés Herrera-Poyatos, Julián Luengo +1
Transformers have become the leading choice in natural language processing over other deep learning architectures. This trend has also permeated the field of time series analysis,…
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability
Iván Sevillano-GarcÃa, Julián Luengo, Francisco Herrera
Out-of-Distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive applications where model failures can have serious consequences. Howeve…
X-SHIELD: Regularization for eXplainable Artificial Intelligence
Iván Sevillano-GarcÃa, Julián Luengo, Francisco Herrera
As artificial intelligence systems become integral across domains, the demand for explainability grows, the called eXplainable artificial intelligence (XAI). Existing efforts prima…