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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 ...
