Deep, Skinny Neural Networks are not Universal Approximators

30 September 2018

Papers citing "Deep, Skinny Neural Networks are not Universal Approximators"

14 / 14 papers shown

Title
Explicit neural network classifiers for non-separable data Patrícia Muñoz Ewald 24 0 0 25 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
Minimum width for universal approximation using ReLU networks on compact domain Namjun Kim Chanho Min Sejun Park VLM 29 10 0 19 Sep 2023
Development of Non-Linear Equations for Predicting Electrical Conductivity in Silicates P. D. Anjos L. A. Quaresma M. Machado 18 0 0 22 May 2023
LU decomposition and Toeplitz decomposition of a neural network Yucong Liu Simiao Jiao Lek-Heng Lim 30 7 0 25 Nov 2022
Minimal Width for Universal Property of Deep RNN Changhoon Song Geonho Hwang Jun ho Lee Myung-joo Kang 25 9 0 25 Nov 2022
A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems Hai V. Nguyen T. Bui-Thanh 39 2 0 09 Aug 2022
Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation Josiah Park Stephan Wojtowytsch 26 1 0 25 Mar 2022
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 36 79 0 17 Sep 2020
Minimum Width for Universal Approximation Sejun Park Chulhee Yun Jaeho Lee Jinwoo Shin 33 122 0 16 Jun 2020
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study David Mickisch F. Assion Florens Greßner W. Günther M. Motta AAML 19 34 0 05 Feb 2020
Stochastic Feedforward Neural Networks: Universal Approximation Thomas Merkh Guido Montúfar 17 8 0 22 Oct 2019
Universal Approximation with Deep Narrow Networks Patrick Kidger Terry Lyons 40 328 0 21 May 2019
Benefits of depth in neural networks Matus Telgarsky 153 603 0 14 Feb 2016