<ul><li>Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between variables.</li><li>Inspired by the success of the iterative magnitude pruning technique in finding lottery tickets of neural networks, a new method called Sparser Random Feature Models via IMP (ShRIMP) is proposed to efficiently fit high-dimensional data with sparse variable dependencies.</li><li>ShRIMP combines the process of constructing and finding sparse lottery tickets for two-layer dense networks.</li><li>Experimental results show that ShRIMP achieves better or comparable test accuracy compared to other sparse feature and additive methods, while offering feature selection with low computational complexity.</li></ul>

How Iterative Magnitude Pruning works part4(AI 2024)

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