Neural Additive and Basis Models with Feature Selection and Interactions
Researchers have developed new neural additive and basis models that incorporate feature selection to improve computational efficiency and model size. These models, proposed by Shinichi Shirakawa, build upon generalized additive models (GAMs) by using neural networks as nonlinear shape functions, offering high interpretability and visualization of feature contributions. The introduction of a feature selection layer addresses the computational bottlenecks previously encountered when dealing with feature interactions or high-dimensional datasets, enabling more efficient training and smaller model sizes while maintaining comparable or better performance than existing GAMs. AI
IMPACT These models offer a more interpretable and computationally efficient approach to deep learning, potentially improving the usability of complex models in various applications.