Complexity of Linear Regions in Deep Networks

25 Jan 2019Boris HaninDavid Rolnick

It is well-known that the expressivity of a neural network depends on its architecture, with deeper networks expressing more complex functions. In the case of networks that compute piecewise linear functions, such as those with ReLU activation, the number of distinct linear regions is a natural measure of expressivity... (read more)

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