Scaling Laws for the Principled Design, Initialization, and Preconditioning of ReLU Networks

ICLR 2020 Aaron DefazioLeon Bottou

Abstract In this work, we describe a set of rules for the design and initialization of well-conditioned neural networks, guided by the goal of naturally balancing the diagonal blocks of the Hessian at the start of training. We show how our measure of conditioning of a block relates to another natural measure of conditioning, the ratio of weight gradients to the weights... (read more)

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