The effect of the choice of neural network depth and breadth on the size of its hypothesis space

6 Jun 2018 · Lech Szymanski, Brendan McCane, Michael Albert ·

We show that the number of unique function mappings in a neural network hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is the number of neurons in the hidden layer $l$.

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The effect of the choice of neural network depth and breadth on the size of its hypothesis space

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