66 citations · 124 across the 22 of their papers we have counts for
Showing 2019 · math.STShow all
2 papers · 2 filters
math.ST2019★ 1 cited
Analysis of the rate of convergence of neural network regression estimates which are easy to implement
Alina Braun, Michael Kohler, Adam Krzyzak
Recent results in nonparametric regression show that for deep learning, i.e., for neural network estimates with many hidden layers, we are able to achieve good rates of convergence…
math.ST2019★ 6 cited
On the rate of convergence of a neural network regression estimate learned by gradient descent
Alina Braun, Michael Kohler, Harro Walk
Nonparametric regression with random design is considered. Estimates are defined by minimzing a penalized empirical risk over a suitably chosen class of neural networks with…