We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously Golowich-Rakhlin-Shamir (2020) obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.

翻译：我们从泛化角度研究学习ReLU神经网络的样本复杂度。给定权重矩阵上的范数约束，常用方法是估计关联函数类的Rademacher复杂度。此前Golowich-Rakhlin-Shamir (2020) 除深度平方根因子外，获得了与网络尺寸无关（按Frobenius范数乘积缩放）的界。我们给出了一种改进，该改进通常完全不具有显式的深度依赖性。