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Why and When Deep is Better than Shallow: Implementation-Agnostic State-Transition Model of Deep Learning
We ask when adding hidden layers improves generalization, in a model that keeps the layers fixed and varies only their number. A hidden layer is a self-map of a state space, a depth-$k$ network composes at most $k$ hidden layers with an output layer, and depth is compared within the nested family $H
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