Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data
DIFFINT is proposed, an autoencoder whose latent bottleneck is structured as a set of soft, axis-aligned interval memberships learned end-to-end directly from raw numerical data, without any discretization or binarization.
Lamine Diop, Marc Plantevit
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