AI RESEARCH
Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
arXiv CS.LG
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ArXi:2509.23068v2 Announce Type: replace-cross Recent advances in deep learning highlight the need for personalized models that can learn from small samples, handle high-dimensional features, and remain interpretable. To address this, we propose the Sparse Deep Additive Model with Interactions (SDAMI), a framework that combines sparsity-driven feature selection with deep subnetworks for flexible function approximation.