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 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.

中文翻译：

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ReLU 网络与大小无关的样本复杂度

我们从泛化的角度研究学习ReLU神经网络的样本复杂度。给定权重矩阵的范数约束，一种常见的方法是估计相关函数类的 Rademacher 复杂度。先前获得了与网络大小无关的界限（除平方根深度因子外）（与 Frobenius 范数的乘积进行缩放）。我们给出的细化通常根本没有明确的深度依赖性。