The GroupMax neural network approximation of convex functions
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Abstract (arXiv)
We present a new neural network to approximate convex functions. This network has the particularity to approximate the function with cuts and can be easily adapted to partial convexity. We give an universal approximation theorem in the full convex case and give many numerical results proving it efficiency.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2022.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025Input Convex Kolmogorov Arnold Networkscited by 5
- 2025Learning Truthful Mechanisms without Discretizationcited by 1
- 2024A new Input Convex Neural Network with application to options pricingcited by 0
- 2025Universal Representation of Generalized Convex Functions and their Gradientscited by 0
- 2026Saddle Networks: Structure-Preserving Architectures for Convex-Concave Functionscited by 0
- 2026Saddle Networks: Structure-Preserving Architectures for Convex-Concave Functions27 May 2026
- 2025Universal Representation of Generalized Convex Functions and their Gradients30 Aug 2025
- 2025Learning Truthful Mechanisms without Discretization28 Jun 2025
- 2025Input Convex Kolmogorov Arnold Networks27 May 2025
- 2024A new Input Convex Neural Network with application to options pricing19 Nov 2024