Deep Network Approximation for Smooth Functions

9 January 2020

Papers citing "Deep Network Approximation for Smooth Functions"

50 / 152 papers shown

Title
Approximation bounds for norm constrained neural networks with applications to regression and GANs Yuling Jiao Yang Wang Yunfei Yang 34 19 0 24 Jan 2022
Theoretical Exploration of Solutions of Feedforward ReLU Networks Changcun Huang 14 4 0 24 Jan 2022
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu Haizhao Yang Minshuo Chen T. Zhao Wenjing Liao 32 36 0 01 Jan 2022
Risk bounds for aggregated shallow neural networks using Gaussian prior L. Tinsi A. Dalalyan BDL 12 7 0 21 Dec 2021
Wasserstein Generative Learning of Conditional Distribution Shiao Liu Xingyu Zhou Yuling Jiao Jian Huang GAN 14 21 0 19 Dec 2021
Approximation of functions with one-bit neural networks C. S. Güntürk Weilin Li 17 8 0 16 Dec 2021
On the rate of convergence of a classifier based on a Transformer encoder Iryna Gurevych Michael Kohler Gözde Gül Sahin 6 11 0 29 Nov 2021
Deep Network Approximation in Terms of Intrinsic Parameters Zuowei Shen Haizhao Yang Shijun Zhang 15 9 0 15 Nov 2021
DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method Zhongjian Wang Jack Xin Zhiwen Zhang 39 15 0 02 Nov 2021
A Review of Physics-based Machine Learning in Civil Engineering S. Vadyala S. N. Betgeri J. Matthews Elizabeth Matthews AI4CE 25 152 0 09 Oct 2021
Stationary Density Estimation of Itô Diffusions Using Deep Learning Yiqi Gu J. Harlim Senwei Liang Haizhao Yang 18 12 0 09 Sep 2021
Estimation of a regression function on a manifold by fully connected deep neural networks Michael Kohler S. Langer U. Reif 20 4 0 20 Jul 2021
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons Zuowei Shen Haizhao Yang Shijun Zhang 43 36 0 06 Jul 2021
Symplectic Learning for Hamiltonian Neural Networks M. David Florian Méhats 13 34 0 22 Jun 2021
Solving PDEs on Unknown Manifolds with Machine Learning Senwei Liang Shixiao W. Jiang J. Harlim Haizhao Yang AI4CE 28 16 0 12 Jun 2021
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks Kexuan Li Fangfang Wang Ruiqi Liu Fan Yang Zuofeng Shang 24 7 0 07 Jun 2021
Universal Regular Conditional Distributions Anastasis Kratsios 15 3 0 17 May 2021
Sparsity-Probe: Analysis tool for Deep Learning Models Ido Ben-Shaul S. Dekel 18 4 0 14 May 2021
ReLU Deep Neural Networks from the Hierarchical Basis Perspective Juncai He Lin Li Jinchao Xu AI4CE 20 30 0 10 May 2021
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors Yuling Jiao Guohao Shen Yuanyuan Lin Jian Huang 28 50 0 14 Apr 2021
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation Q. Du Yiqi Gu Haizhao Yang Chao Zhou 24 20 0 21 Mar 2021
Evolutional Deep Neural Network Yifan Du T. Zaki 16 68 0 18 Mar 2021
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class Yuling Jiao Yanming Lai Xiliang Lu Fengru Wang J. Yang Yuanyuan Yang 6 3 0 28 Feb 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 101 115 0 28 Feb 2021
Quantitative approximation results for complex-valued neural networks A. Caragea D. Lee J. Maly G. Pfander F. Voigtlaender 11 5 0 25 Feb 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 21 7 0 30 Jan 2021
On the capacity of deep generative networks for approximating distributions Yunfei Yang Zhen Li Yang Wang 17 28 0 29 Jan 2021
Reproducing Activation Function for Deep Learning Senwei Liang Liyao Lyu Chunmei Wang Haizhao Yang 28 21 0 13 Jan 2021
Strong overall error analysis for the training of artificial neural networks via random initializations Arnulf Jentzen Adrian Riekert 6 3 0 15 Dec 2020
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 34 29 0 11 Dec 2020
The universal approximation theorem for complex-valued neural networks F. Voigtlaender 14 62 0 06 Dec 2020
On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data Michael Kohler A. Krzyżak 8 12 0 31 Oct 2020
Neural Network Approximation: Three Hidden Layers Are Enough Zuowei Shen Haizhao Yang Shijun Zhang 19 114 0 25 Oct 2020
Exponential ReLU Neural Network Approximation Rates for Point and Edge Singularities C. Marcati J. Opschoor P. Petersen Christoph Schwab 6 29 0 23 Oct 2020
Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice Andrii Babii Xi Chen Eric Ghysels Rohit Kumar FaML 11 10 0 16 Oct 2020
Approximating smooth functions by deep neural networks with sigmoid activation function S. Langer 19 66 0 08 Oct 2020
The Kolmogorov-Arnold representation theorem revisited Johannes Schmidt-Hieber 30 125 0 31 Jul 2020
On Representing (Anti)Symmetric Functions Marcus Hutter 9 22 0 30 Jul 2020
Maximum-and-Concatenation Networks Xingyu Xie Hao Kong Jianlong Wu Wayne Zhang Guangcan Liu Zhouchen Lin 75 2 0 09 Jul 2020
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory Tao Luo Haizhao Yang 21 73 0 28 Jun 2020
Deep Network with Approximation Error Being Reciprocal of Width to Power of Square Root of Depth Zuowei Shen Haizhao Yang Shijun Zhang 6 7 0 22 Jun 2020
Approximation in shift-invariant spaces with deep ReLU neural networks Yunfei Yang Zhen Li Yang Wang 26 14 0 25 May 2020
On Deep Instrumental Variables Estimate Ruiqi Liu Zuofeng Shang Guang Cheng 21 25 0 30 Apr 2020
Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices Calypso Herrera Florian Krach Anastasis Kratsios P. Ruyssen Josef Teichmann 20 2 0 28 Apr 2020
Numerical Solution of the Parametric Diffusion Equation by Deep Neural Networks Moritz Geist P. Petersen Mones Raslan R. Schneider Gitta Kutyniok 18 83 0 25 Apr 2020
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions Yulong Lu Jianfeng Lu 10 19 0 19 Apr 2020
The gap between theory and practice in function approximation with deep neural networks Ben Adcock N. Dexter 7 93 0 16 Jan 2020
Machine Learning for Prediction with Missing Dynamics J. Harlim Shixiao W. Jiang Senwei Liang Haizhao Yang AI4CE 12 60 0 13 Oct 2019
Deep Network Approximation Characterized by Number of Neurons Zuowei Shen Haizhao Yang Shijun Zhang 18 182 0 13 Jun 2019
Nonlinear Approximation and (Deep) ReLU Networks Ingrid Daubechies Ronald A. DeVore S. Foucart Boris Hanin G. Petrova 17 138 0 05 May 2019