The universal approximation theorem for complex-valued neural networks

6 December 2020

Papers citing "The universal approximation theorem for complex-valued neural networks"

19 / 19 papers shown

Title
Kolmogorov-Arnold Network Autoencoders Mohammadamin Moradi Shirin Panahi Erik Bollt Ying-Cheng Lai 23 5 0 02 Oct 2024
CoNO: Complex Neural Operator for Continous Dynamical Physical Systems Karn Tiwari N. M. A. Krishnan P. PrathoshA 26 0 0 01 Jun 2024
Neural Controlled Differential Equations with Quantum Hidden Evolutions Lingyi Yang Zhen Shao 29 0 0 30 Apr 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts Anastasis Kratsios Haitz Sáez de Ocáriz Borde Takashi Furuya Marc T. Law MoE 41 1 0 05 Feb 2024
Do stable neural networks exist for classification problems? -- A new view on stability in AI Z. N. D. Liu A. C. Hansen 22 0 0 15 Jan 2024
Universal Approximation Theorem for Vector- and Hypercomplex-Valued Neural Networks Marcos Eduardo Valle Wington L. Vital Guilherme Vieira 15 7 0 04 Jan 2024
On the Computational Complexities of Complex-valued Neural Networks K. S. Mayer J. A. Soares Ariadne A. Cruz D. Arantes 21 2 0 19 Oct 2023
Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks Marcos Eduardo Valle 19 3 0 14 Sep 2023
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere Boris Bonev Thorsten Kurth Christian Hundt Jaideep Pathak Maximilian Baust K. Kashinath Anima Anandkumar AI4Cl AI4CE 16 123 0 06 Jun 2023
Universal approximation with complex-valued deep narrow neural networks Paul Geuchen Thomas Jahn Hannes Matt 13 3 0 26 May 2023
Selected aspects of complex, hypercomplex and fuzzy neural networks A. Niemczynowicz R. Kycia Maciej Jaworski A. Siemaszko J. Calabuig ... Baruch Schneider Diana Berseghyan Irina Perfiljeva V. Novák Piotr Artiemjew 19 0 0 29 Dec 2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 23 34 0 06 Dec 2022
On the Approximation and Complexity of Deep Neural Networks to Invariant Functions Gao Zhang Jin-Hui Wu Shao-Qun Zhang 16 0 0 27 Oct 2022
Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation Josiah Park Stephan Wojtowytsch 23 1 0 25 Mar 2022
Theoretical Exploration of Flexible Transmitter Model Jin-Hui Wu Shao-Qun Zhang Yuan Jiang Zhiping Zhou 33 3 0 11 Nov 2021
Towards Understanding Theoretical Advantages of Complex-Reaction Networks Shao-Qun Zhang Gaoxin Wei Zhi-Hua Zhou 15 17 0 15 Aug 2021
Invariant polynomials and machine learning W. Haddadin 34 7 0 26 Apr 2021
Scaling of neural-network quantum states for time evolution Sheng-Hsuan Lin F. Pollmann 19 25 0 21 Apr 2021
Quantitative approximation results for complex-valued neural networks A. Caragea D. Lee J. Maly G. Pfander F. Voigtlaender 13 5 0 25 Feb 2021