Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport

Shayan Hundrieser, Insung Kong, Johannes Schmidt-Hieber|April 29, 2026arXiv

Key Takeaway

HyCNNs are a more parameter-efficient way to build neural networks that must output convex functions, requiring exponentially fewer parameters than previous methods while maintaining theoretical guarantees.

Summary

This paper introduces Hyper Input Convex Neural Networks (HyCNNs), a new neural network architecture that guarantees convex outputs while using far fewer parameters than existing methods.

architecture efficiency

Key Terms

input-convex-neural-network convex-function optimal-transport maxout-network