Approximation capability of two hidden layer feedforward neural networks with fixed weights

22 January 2021

Papers citing "Approximation capability of two hidden layer feedforward neural networks with fixed weights"

21 / 21 papers shown

Title
Expressivity of Quadratic Neural ODEs Joshua Hanson Maxim Raginsky 26 0 0 13 Apr 2025
On shallow feedforward neural networks with inputs from a topological space Vugar Ismailov 54 0 0 03 Apr 2025
Approximation properties of neural ODEs Arturo De Marinis Davide Murari E. Celledoni Nicola Guglielmi B. Owren Francesco Tudisco 52 1 0 19 Mar 2025
Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis Hyunwoo Lee Hayoung Choi Hyunju Kim 44 1 0 03 Oct 2024
Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured? Li Liu Tengchao Yu Heng Yong 17 0 0 25 Sep 2024
Universal approximation theorem for neural networks with inputs from a topological vector space Vugar Ismailov 44 1 0 19 Sep 2024
Addressing common misinterpretations of KART and UAT in neural network literature Vugar Ismailov HAI 65 1 0 29 Aug 2024
SGD method for entropy error function with smoothing l0 regularization for neural networks Trong-Tuan Nguyen Van-Dat Thang N. Thin Phuong T. Nguyen 19 1 0 28 May 2024
Fingerprinting web servers through Transformer-encoded HTTP response headers Patrick Darwinkel 18 0 0 26 Mar 2024
Efficient Computation of Confidence Sets Using Classification on Equidistributed Grids Lujie Zhou 20 0 0 03 Jan 2024
On the Kolmogorov neural networks Aysu Ismayilova V. Ismailov 39 17 0 31 Oct 2023
KASAM: Spline Additive Models for Function Approximation H. V. Deventer P. V. Rensburg Anna Sergeevna Bosman KELM CLL 24 3 0 12 May 2022
Approximation error of single hidden layer neural networks with fixed weights V. Ismailov MLT 11 6 0 07 Feb 2022
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 34 138 0 18 Apr 2021
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation Q. Du Yiqi Gu Haizhao Yang Chao Zhou 26 20 0 21 Mar 2021
Elementary superexpressive activations Dmitry Yarotsky 27 35 0 22 Feb 2021
A three layer neural network can represent any multivariate function V. Ismailov 26 15 0 05 Dec 2020
Neural Network Approximation: Three Hidden Layers Are Enough Zuowei Shen Haizhao Yang Shijun Zhang 35 115 0 25 Oct 2020
The Kolmogorov-Arnold representation theorem revisited Johannes Schmidt-Hieber 30 126 0 31 Jul 2020
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation Arnulf Jentzen Timo Welti 27 15 0 03 Mar 2020
Negative results for approximation using single layer and multilayer feedforward neural networks J. M. Almira P. E. López-de-Teruel D. J. Romero-Lopez F. Voigtlaender MLT 13 2 0 23 Oct 2018