[PDF][PDF] A non-parametric regression viewpoint: Generalization of overparametrized deep ReLU network under noisy observations
We study the generalization properties of the overparameterized deep neural network
(DNN) with Rectified Linear Unit (ReLU) activations. Under the nonparametric regression
framework, it is assumed that the ground-truth function is from a reproducing kernel Hilbert
space (RKHS) induced by a neural tangent kernel (NTK) of ReLU DNN, and a dataset is
given with the noises. Without a delicate adoption of early stopping, we prove that the
overparametrized DNN trained by vanilla gradient descent does not recover the ground-truth …
(DNN) with Rectified Linear Unit (ReLU) activations. Under the nonparametric regression
framework, it is assumed that the ground-truth function is from a reproducing kernel Hilbert
space (RKHS) induced by a neural tangent kernel (NTK) of ReLU DNN, and a dataset is
given with the noises. Without a delicate adoption of early stopping, we prove that the
overparametrized DNN trained by vanilla gradient descent does not recover the ground-truth …
以上显示的是最相近的搜索结果。 查看全部搜索结果