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cs.LG2023
Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data
Jonathan W. Siegel
We study the interpolation power of deep ReLU neural networks. Specifically, we consider the question of how efficiently, in terms of the number of parameters, deep ReLU networks c…
cs.LG2022★ 9 cited
On the Activation Function Dependence of the Spectral Bias of Neural Networks
Qingguo Hong, Jonathan W. Siegel, Qinyang Tan +1
Neural networks are universal function approximators which are known to generalize well despite being dramatically overparameterized. We study this phenomenon from the point of vie…