Articles by Hugo Thimonier

TracInAD: Measuring Influence for Anomaly Detection

-- Hugo Thimonier (LISN, Galac)

summary: As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This talk proposes a novel methodology to flag anomalies based on TracIn, an influence measure initially introduced for explicability purposes. The proposed ...