%0 Journal Article %1 kohler2019overparametrized %A Kohler, Michael %A Krzyzak, Adam %D 2019 %K bounds deep-learning generalization readings %T Over-parametrized deep neural networks do not generalize well %U http://arxiv.org/abs/1912.03925 %X Recently it was shown in several papers that backpropagation is able to find the global minimum of the empirical risk on the training data using over-parametrized deep neural networks. In this paper a similar result is shown for deep neural networks with the sigmoidal squasher activation function in a regression setting, and a lower bound is presented which proves that these networks do not generalize well on a new data in the sense that they do not achieve the optimal minimax rate of convergence for estimation of smooth regression functions.

@article{kohler2019overparametrized, abstract = {Recently it was shown in several papers that backpropagation is able to find the global minimum of the empirical risk on the training data using over-parametrized deep neural networks. In this paper a similar result is shown for deep neural networks with the sigmoidal squasher activation function in a regression setting, and a lower bound is presented which proves that these networks do not generalize well on a new data in the sense that they do not achieve the optimal minimax rate of convergence for estimation of smooth regression functions.}, added-at = {2019-12-11T00:31:03.000+0100}, author = {Kohler, Michael and Krzyzak, Adam}, biburl = {https://www.bibsonomy.org/bibtex/29afc566941c101707374f7bde46eba31/kirk86}, description = {[1912.03925] Over-parametrized deep neural networks do not generalize well}, interhash = {68a12a7ee8544c7fbca02a0f4a9743c2}, intrahash = {9afc566941c101707374f7bde46eba31}, keywords = {bounds deep-learning generalization readings}, note = {cite arxiv:1912.03925}, timestamp = {2019-12-11T00:31:03.000+0100}, title = {Over-parametrized deep neural networks do not generalize well}, url = {http://arxiv.org/abs/1912.03925}, year = 2019 }