2022/09/19 by James Enouen, Yan Liu, Enouen, James +1 · 1 voice · 7 citations
Computer Science · Mathematics · #Advanced Neural Network Applications #Artificial intelligence #Artificial neural network #Computer science #Construct (python library) #Deep neural networks #Domain Adaptation and Few-Shot Learning #Feature (linguistics) #Feature selection #Generalizability theory #Machine Learning and ELM #Machine learning #Mathematics #Pattern recognition (psychology) #Selection (genetic algorithm) #Simple (philosophy)
paper · pdf · doi:10.48550/arxiv.2209.09326
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and the powerful deep methods using neural networks. 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. Our proposed Sparse Interaction Additive Networks (SIAN) construct a bridge from these simple and interpretable models to fully connected neural networks. SIAN achieves competitive performance against state-of-the-art methods across multiple large-scale tabular datasets and consistently finds an optimal tradeoff between the modeling capacity of neural networks and the generalizability of simpler methods.