The effect of the choice of neural network depth and breadth on the size of its hypothesis space
classification
💻 cs.LG
stat.ML
keywords
hypothesisnetworkneuralnumberspacebreadthchoicedepth
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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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