SDAMI detects interactions in high-dimensional data via an Effect Footprint principle and models them using sparsity, group lasso, and dedicated deep subnetworks for improved interpretability.
Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces
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Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
SDAMI detects interactions in high-dimensional data via an Effect Footprint principle and models them using sparsity, group lasso, and dedicated deep subnetworks for improved interpretability.