On the Inductive Bias of Neural Tangent Kernels

NeurIPS 2019 Alberto BiettiJulien Mairal

State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties. A recent line of work has shown that in a certain over-parameterized regime, the learning dynamics of gradient descent are governed by a certain kernel obtained at initialization, called the neural tangent kernel... (read more)

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