Fokina, DariaDariaFokinaOseledets, Ivan V.Ivan V.Oseledets2023-09-272023-09-272023https://publica.fraunhofer.de/handle/publica/45103010.1515/rnam-2023-00012-s2.0-85148758510We propose a new method for learning deep neural network models, which is based on a greedy learning approach: we add one basis function at a time, and a new basis function is generated as a non-linear activation function applied to a linear combination of the previous basis functions. Such a method (growing deep neural network by one neuron at a time) allows us to compute much more accurate approximants for several model problems in function approximation.enDeep ReLU networksfunction approximationgreedy approximationjournal article