论文检索

全连接深度神经网络回归估计的收敛速率AI 解读

Recent results in nonparametric regression show that deep learning, that is, neural network estimates with many hidden layers, are able to circumvent the so-called curse of dimensionality in case that suitable restrictions on the structure of the regression function hold. One key feature of the neural networks used in these results is that their network architecture has a further constraint, namely the network sparsity. In this paper, we show that we can get similar results…

关于“使用 ReLU 深度神经网络进行非参数回归”的讨论AI 解读

First we would like to congratulate Professor Johannes Schmidt-Hieber for his excellent paper, which shows the surprising result that deep neural networks can achieve good rates of convergence even in case of nonsmooth activation functions.In the following we divide our discussion into three parts:1.The importance of compository assumptions.2. The necessity of the sparsity of the networks.3. The theoretical difference between ReLU and sigmoidal functions.