
Differentiable RePU neural networks can accurately represent multivariate polynomials and simultaneously approximate smooth functions and their derivatives, with improved approximation rates when the data has a low-dimensional structure. These properties enable applications in score estimation and isotonic regression.


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approximation-properties-of-differentiable-neural-networks-with-rectified-power-unit-activation-and-applications-to-score-estimation-and-isotonic-regression


Approximation Properties of Differentiable Neural Networks with Rectified Power Unit Activation and Applications to Score Estimation and Isotonic Regression
