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Poster
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Yuan Cao · Quanquan Gu

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #141
in Deep Learning -- Optimization for Deep Networks »
We study the training and generalization of deep neural networks (DNNs) in the over-parameterized regime, where the network width (i.e., number of hidden nodes per layer) is much larger than the number of training data points. We show that, the expected $0$-$1$ loss of a wide enough ReLU network trained with stochastic gradient descent (SGD) and random initialization can be bounded by the training loss of a random feature model induced by the network gradient at initialization, which we call a \textit{neural tangent random feature} (NTRF) model. For data distributions that can be classified by NTRF model with sufficiently small error, our result yields a generalization error bound in the order of $\tilde{\mathcal{O}}(n^{-1/2})$ that is independent of the network width. Our result is more general and sharper than many existing generalization error bounds for over-parameterized neural networks. In addition, we establish a strong connection between our generalization error bound and the neural tangent kernel (NTK) proposed in recent work.
Keywords: Learning Theory · Deep Learning · Theory
[ Paper]  [ Slides] 

Author Information

Yuan Cao (UCLA)
Quanquan Gu (UCLA)

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