3 papers
cs.CV2026
Too much of a good thing -- when knowledge distillation promotes overfitting, and how to avoid it
Irene Trigueros-Lorca, Leonardo Concepción, Christian Wagner +2
The growing size of Convolutional Neural Networks has led to increasingly large and costly models. Knowledge Distillation (KD) addresses this by transferring knowledge from a large…
cs.LG2026
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…
cs.LG2026
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…