Kernel and Rich Regimes in Overparametrized Models

13 Jun 2019Blake WoodworthSuriya GunasekarPedro SavareseEdward MoroshkoItay GolanJason LeeDaniel SoudryNathan Srebro

A recent line of work studies overparametrized neural networks in the "kernel regime," i.e. when the network behaves during training as a kernelized linear predictor, and thus training with gradient descent has the effect of finding the minimum RKHS norm solution. This stands in contrast to other studies which demonstrate how gradient descent on overparametrized multilayer networks can induce rich implicit biases that are not RKHS norms... (read more)

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