slack" and continue the path from this argument. We continue this process until either 1) we reach a

˜f is Lipschitz:| ˜f(t2)− ˜f(t1)|=|f(x 0 +t 2ˆv)−f(x 0 +t 1ˆv)|≤L∥x 0 +t 2ˆv−x 0 +t 1ˆv∥=L|t 2−t 1|∥ˆv∥= L|t2−t 1| 2 · 2017

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Training Deep Morphological Neural Networks as Universal Approximators

cs.LG · 2025-05-14 · conditional · novelty 7.0

Pure max-plus/min-plus deep networks are shown to be the opposite of universal approximators, while inserting O(N)-parameter linear scalings per layer yields provably universal morphological networks.

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Training Deep Morphological Neural Networks as Universal Approximators cs.LG · 2025-05-14 · conditional · none · ref 1
Pure max-plus/min-plus deep networks are shown to be the opposite of universal approximators, while inserting O(N)-parameter linear scalings per layer yields provably universal morphological networks.

slack" and continue the path from this argument. We continue this process until either 1) we reach a

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