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。在权重矩阵存在范数约束的条件下，常用方法是估计相应函数类的拉德马赫复杂度。此前Golowich-Rakhlin-Shamir（2020）得到了一个与网络规模无关（以Frobenius范数乘积为度量）的界，但存在深度平方根因子的影响。我们给出的改进版本通常完全不存在显式的深度依赖性。