Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection

Neural Information Processing Systems 

Previous works attempting to close this gap have failed to fully investigate the exponentially growing number of feature combinations which deep networks consider automatically during training. In this work, we develop a tractable selection algorithm to efficiently identify the necessary feature combinations by leveraging techniques in feature interaction detection.

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