Neural Network with Unbounded Activation Functions is Universal Approximator

14 May 2015

Papers citing "Neural Network with Unbounded Activation Functions is Universal Approximator"

50 / 109 papers shown

Title
Universal Approximation Theorem for Neural Networks Takato Nishijima 11 13 0 19 Feb 2021
Universal Approximation Properties for an ODENet and a ResNet: Mathematical Analysis and Numerical Experiments Yuto Aizawa M. Kimura Kazunori Matsui 6 2 0 22 Dec 2020
Physical deep learning based on optimal control of dynamical systems Genki Furuhata T. Niiyama S. Sunada PINN AI4CE 29 14 0 16 Dec 2020
A global universality of two-layer neural networks with ReLU activations N. Hatano Masahiro Ikeda Isao Ishikawa Y. Sawano 8 5 0 20 Nov 2020
Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise Behrouz Azimian R. Biswas A. Pal L. Tong 14 7 0 09 Nov 2020
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity Yuuki Takai Akiyoshi Sannai Matthieu Cordonnier 80 4 0 23 Oct 2020
A Sequential Framework Towards an Exact SDP Verification of Neural Networks Ziye Ma Somayeh Sojoudi 34 8 0 16 Oct 2020
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks Takuo Matsubara Chris J. Oates F. Briol BDL UQCV 21 17 0 16 Oct 2020
How Powerful are Shallow Neural Networks with Bandlimited Random Weights? Ming Li Sho Sonoda Feilong Cao Yu Wang Jiye Liang 11 7 0 19 Aug 2020
Deep learning for photoacoustic imaging: a survey Changchun Yang Hengrong Lan Feng Gao Fei Gao VLM MedIm 22 21 0 10 Aug 2020
Theory of Deep Convolutional Neural Networks II: Spherical Analysis Zhiying Fang Han Feng Shuo Huang Ding-Xuan Zhou 45 37 0 28 Jul 2020
No one-hidden-layer neural network can represent multivariable functions Masayo Inoue Mana Futamura H. Ninomiya MLT 13 0 0 19 Jun 2020
Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann Julien N. P. Martel Alexander W. Bergman David B. Lindell Gordon Wetzstein AI4TS 47 2,490 0 17 Jun 2020
Solving Differential Equations Using Neural Network Solution Bundles Cedric Wen Flamant P. Protopapas David Sondak 20 29 0 17 Jun 2020
Banach Space Representer Theorems for Neural Networks and Ridge Splines Rahul Parhi Robert D. Nowak 10 7 0 10 Jun 2020
Activation functions are not needed: the ratio net Chi-Chun Zhou Hai-Long Tu Yue-Jie Hou Zhen Ling Yi Liu Jian Hua 24 0 0 14 May 2020
A survey on modern trainable activation functions Andrea Apicella Francesco Donnarumma Francesco Isgrò R. Prevete 36 366 0 02 May 2020
Nonconvex regularization for sparse neural networks Konstantin Pieper Armenak Petrosyan 21 7 0 24 Apr 2020
A function space analysis of finite neural networks with insights from sampling theory Raja Giryes 22 6 0 15 Apr 2020
Symmetry & critical points for a model shallow neural network Yossi Arjevani M. Field 36 13 0 23 Mar 2020
Universal Function Approximation on Graphs Rickard Brüel-Gabrielsson 32 6 0 14 Mar 2020
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity Nikolaos N. Vlassis R. Ma WaiChing Sun AI4CE 13 170 0 08 Jan 2020
Machine Learning from a Continuous Viewpoint E. Weinan Chao Ma Lei Wu 33 102 0 30 Dec 2019
Misspecified diffusion models with high-frequency observations and an application to neural networks Teppei Ogihara 11 3 0 26 Dec 2019
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space Taiji Suzuki Atsushi Nitanda 21 61 0 28 Oct 2019
Neural network integral representations with the ReLU activation function Armenak Petrosyan Anton Dereventsov Clayton Webster 15 22 0 07 Oct 2019
On Universal Equivariant Set Networks Nimrod Segol Y. Lipman 3DPC 25 63 0 06 Oct 2019
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case Greg Ongie Rebecca Willett Daniel Soudry Nathan Srebro 13 160 0 03 Oct 2019
Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network Taiji Suzuki Hiroshi Abe Tomoaki Nishimura AI4CE 25 43 0 25 Sep 2019
Effect of Activation Functions on the Training of Overparametrized Neural Nets A. Panigrahi Abhishek Shetty Navin Goyal 19 20 0 16 Aug 2019
Fast generalization error bound of deep learning without scale invariance of activation functions Y. Terada Ryoma Hirose MLT 19 6 0 25 Jul 2019
A Fine-Grained Spectral Perspective on Neural Networks Greg Yang Hadi Salman 35 111 0 24 Jul 2019
Copula Representations and Error Surface Projections for the Exclusive Or Problem R. Freedman 9 0 0 08 Jul 2019
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs B. Grimstad H. Andersson 24 139 0 06 Jul 2019
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification Kenta Oono Taiji Suzuki GNN 32 27 0 27 May 2019
Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks Anton Dereventsov Armenak Petrosyan Clayton Webster 15 9 0 24 May 2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems Atsushi Nitanda Geoffrey Chinot Taiji Suzuki MLT 16 33 0 23 May 2019
On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces Satoshi Hayakawa Taiji Suzuki 6 48 0 22 May 2019
Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven Gabriel Peyré 33 287 0 13 May 2019
Universal approximations of permutation invariant/equivariant functions by deep neural networks Akiyoshi Sannai Yuuki Takai Matthieu Cordonnier 29 67 0 05 Mar 2019
A simple and efficient architecture for trainable activation functions Andrea Apicella Francesco Isgrò R. Prevete 8 36 0 08 Feb 2019
Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models Sho Sonoda 21 0 0 02 Feb 2019
Knots in random neural networks Kevin K. Chen A. Gamst Alden Walker 30 4 0 27 Nov 2018
An overview of deep learning in medical imaging focusing on MRI A. Lundervold A. Lundervold OOD 22 1,608 0 25 Nov 2018
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning Kursat Rasim Mestav Jaime Luengo-Rozas L. Tong BDL 34 133 0 07 Nov 2018
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality Taiji Suzuki 25 243 0 18 Oct 2018
A Gentle Introduction to Deep Learning in Medical Image Processing Andreas Maier Christopher Syben Tobias Lasser Christian Riess PINN 11 427 0 12 Oct 2018
Neural Networks Trained to Solve Differential Equations Learn General Representations M. Magill F. Qureshi H. W. Haan 14 64 0 29 Jun 2018
On the Spectral Bias of Neural Networks Nasim Rahaman A. Baratin Devansh Arpit Felix Dräxler Min Lin Fred Hamprecht Yoshua Bengio Aaron Courville 57 1,395 0 22 Jun 2018
The global optimum of shallow neural network is attained by ridgelet transform Sho Sonoda Isao Ishikawa Masahiro Ikeda Kei Hagihara Y. Sawano Takuo Matsubara Noboru Murata 14 1 0 19 May 2018