A ReLU network is convex iff certain non-negative path products hold, and unlike one-hidden-layer networks, two-hidden-layer convex networks can escape the ICNN constraint.
In: Proceedings of the IEEE International Conference on Computer Vision, pp
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Convexity in ReLU Neural Networks: beyond ICNNs?
A ReLU network is convex iff certain non-negative path products hold, and unlike one-hidden-layer networks, two-hidden-layer convex networks can escape the ICNN constraint.