Statistical learning by sparse deep neural networks
Felix Abramovich
We consider a deep neural network estimator based on empirical risk
minimization with l_1-regularization. We derive a general bound for its excess
risk in regression and classification (including multiclass), and prove that it
is adaptively nearly-minimax (up to log-factors) simultaneously across the
entire range of various function classes.