For regression functions that are locally low-dimensional, sparse neural network estimates achieve the rate n^{-2p/(2p+d*)}, with the exponent depending only on the local dimension d* and not the input dimension d.
Corollary 1 concerns the result of Theorem 2, if we choose J1 = ∅ in (20)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2019 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Estimation of a function of low local dimensionality by deep neural networks
For regression functions that are locally low-dimensional, sparse neural network estimates achieve the rate n^{-2p/(2p+d*)}, with the exponent depending only on the local dimension d* and not the input dimension d.