TabNet: Attentive Interpretable Tabular Learning

ICLR 2020 Sercan O. ArikTomas Pfister

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features... (read more)

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