Theoretical Deep Learning #2: Worst-case bounds. Part 4
We complete with bounding test-train risk difference of a deep fully-connected network with 1-Lipschitz non-linearity. Resulting bound grows with Lipschitz constant of the learned network and with number of layers.
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