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We investigate how shallow ReLU networks interpolate between known regions.Our analysis shows that empirical risk minimizers converge to a minimum norminterpolant as the number of data points and parameters tends to infinity whena weight decay regularizer is penalized with a coefficient which vanishes at aprecise rate as the network width and the number of data points grow. With andwithout explicit regularization, we numerically study the implicit bias ofcommon optimization algorithms towards known minimum norm interpolants.

Minimum norm interpolation by perceptra: Explicit regularization and implicit bias