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Wide neural networks with linear output layer have been shown to benear-linear, and to have near-constant neural tangent kernel (NTK), in a regioncontaining the optimization path of gradient descent. These findings seemcounter-intuitive since in general neural networks are highly complex models.Why does a linear structure emerge when the networks become wide? In this work,we provide a new perspective on this "transition to linearity" by considering aneural network as an assembly model recursively built from a set of sub-modelscorresponding to individual neurons. In this view, we show that the linearityof wide neural networks is, in fact, an emerging property of assembling a largenumber of diverse "weak" sub-models, none of which dominate the assembly.

Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models