Before revision:   We show that the sparse random feature method outperforms shallow networks for well-structured functions and applications to scientific machine learning tasks.
After revision:  In particular, by introducing sparse features, i.e. features with random sparse weights, we provide improved bounds for low order functions. We show that the sparse random feature method outperforms shallow networks for well-structured functions and applications to scientific machine learning tasks.