SOC-ICNN generalizes ReLU-based ICNNs to SOCP, strictly expanding the class of representable convex functions while preserving similar forward-pass complexity.
Input convex lipschitz recurrent neural networks for robust and efficient process model- ing and optimization.arXiv preprint arXiv:2401.07494, January
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SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
SOC-ICNN generalizes ReLU-based ICNNs to SOCP, strictly expanding the class of representable convex functions while preserving similar forward-pass complexity.