https://arxiv.org/abs/2311.00049 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2311.00049 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Neural and Evolutionary Computing arXiv:2311.00049 (cs) [Submitted on 31 Oct 2023] Title:On the Kolmogorov neural networks Authors:Aysu Ismayilova, Vugar Ismailov Download a PDF of the paper titled On the Kolmogorov neural networks, by Aysu Ismayilova and Vugar Ismailov Download PDF Abstract:In this paper, we show that the Kolmogorov two hidden layer neural network model with a continuous, discontinuous bounded or unbounded activation function in the second hidden layer can precisely represent continuous, discontinuous bounded and all unbounded multivariate functions, respectively. Comments: 14 pages, 1 figure; this article uses material from arXiv:2012.03016 Neural and Evolutionary Computing (cs.NE); Machine Learning Subjects: (cs.LG); Functional Analysis (math.FA); Machine Learning (stat.ML) MSC 46A22, 46E10, 46N60, 68T05, 92B20 classes: Cite as: arXiv:2311.00049 [cs.NE] (or arXiv:2311.00049v1 [cs.NE] for this version) https://doi.org/10.48550/arXiv.2311.00049 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Vugar Ismailov [view email] [v1] Tue, 31 Oct 2023 18:01:58 UTC (206 KB) Full-text links: Access Paper: Download a PDF of the paper titled On the Kolmogorov neural networks, by Aysu Ismayilova and Vugar Ismailov * Download PDF * PostScript * Other Formats (view license) Current browse context: cs.NE < prev | next > new | recent | 2311 Change to browse by: cs cs.LG math math.FA stat stat.ML References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Reddit logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) [ ] scite.ai Toggle scite Smart Citations (What are Smart Citations?) ( ) Code, Data, Media Code, Data and Media Associated with this Article [ ] Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) [ ] DagsHub Toggle DagsHub (What is DagsHub?) [ ] Links to Code Toggle Papers with Code (What is Papers with Code?) [ ] ScienceCast Toggle ScienceCast (What is ScienceCast?) ( ) Demos Demos [ ] Replicate Toggle Replicate (What is Replicate?) [ ] Spaces Toggle Hugging Face Spaces (What is Spaces?) ( ) Related Papers Recommenders and Search Tools [ ] Link to Influence Flower Influence Flower (What are Influence Flowers?) [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) * Author * Venue * Institution * Topic ( ) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack