Kolmogorov--Arnold stability
Not on the map. Why?
Abstract (arXiv)
Regarding the representation theorem of Kolmogorov and Arnold (KA) as an algorithm for representing or <<expressing>> functions, we test its robustness by analyzing its stability to withstand re-parameterizations of the hidden space. One may think of such re-parameterizations as the work of an adversary attempting to foil the construction of the KA outer function. We find KA to be stable under countable collections of continuous re-parameterizations, but unearth a question about the equi-continuity of the outer functions that, so far, obstructs taking limits and defeating continuous groups of re-parameterizations. This question on the regularity of the outer functions is relevant to the debate over the applicability of KA to the general theory of NNs.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 7 Apr 2025.
Builds on
3 of 19 references.
References · 3
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2024KAN: Kolmogorov-Arnold Networkscited by 649
- 2024The Proof of Kolmogorov-Arnold May Illuminate Neural Network Learningcited by 3
Built on by
Citing papers per year: 2026: 3.
3 papers.