Lipschitz neural networks are dense in the set of all Lipschitz functions

2020/09/29 by Stephan Eckstein, Eckstein, Stephan · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Numerical Analysis (math.NA)

paper · pdf · doi:10.48550/arxiv.2009.13881

openalex publication_date 2020/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This note shows that, for a fixed Lipschitz constant L > 0, one layer neural networks that are L-Lipschitz are dense in the set of all L-Lipschitz functions with respect to the uniform norm on bounded sets.

Citations

• On the regularization of Wasserstein GANs

Cited by

• Hybrid Ridgelet Deep Neural Networks for Data-Driven Arbitrage Strategies

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