Error bounds for approximations with deep ReLU networks

3 October 2016

Papers citing "Error bounds for approximations with deep ReLU networks"

50 / 213 papers shown

Title
On Representing (Anti)Symmetric Functions Marcus Hutter 17 22 0 30 Jul 2020
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems Donsub Rim Luca Venturi Joan Bruna Benjamin Peherstorfer 28 9 0 28 Jul 2020
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction Yunmei Chen Hongcheng Liu X. Ye Qingchao Zhang 56 23 0 22 Jul 2020
Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions Philipp Grohs L. Herrmann 30 52 0 10 Jul 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 51 0 09 Jul 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs Siddhartha Mishra Roberto Molinaro PINN 25 171 0 29 Jun 2020
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory Tao Luo Haizhao Yang 32 73 0 28 Jun 2020
Layer Sparsity in Neural Networks Mohamed Hebiri Johannes Lederer 36 10 0 28 Jun 2020
A block coordinate descent optimizer for classification problems exploiting convexity Ravi G. Patel N. Trask Mamikon A. Gulian E. Cyr ODL 30 7 0 17 Jun 2020
Globally Injective ReLU Networks Michael Puthawala K. Kothari Matti Lassas Ivan Dokmanić Maarten V. de Hoop 24 26 0 15 Jun 2020
Learning the geometry of wave-based imaging K. Kothari Maarten V. de Hoop Ivan Dokmanić AI4CE 27 8 0 10 Jun 2020
Approximation in shift-invariant spaces with deep ReLU neural networks Yunfei Yang Zhen Li Yang Wang 34 14 0 25 May 2020
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation Arnulf Jentzen Timo Welti 17 15 0 03 Mar 2020
Uncertainty Quantification for Sparse Deep Learning Yuexi Wang Veronika Rockova BDL UQCV 31 31 0 26 Feb 2020
A closer look at the approximation capabilities of neural networks Kai Fong Ernest Chong 13 16 0 16 Feb 2020
Deep Network Approximation for Smooth Functions Jianfeng Lu Zuowei Shen Haizhao Yang Shijun Zhang 67 247 0 09 Jan 2020
Deep Learning via Dynamical Systems: An Approximation Perspective Qianxiao Li Ting Lin Zuowei Shen AI4TS AI4CE 22 107 0 22 Dec 2019
iPromoter-BnCNN: a Novel Branched CNN Based Predictor for Identifying and Classifying Sigma Promoters Ruhul Amin C. R. Rahman Md. Habibur Rahman Sifat Md Nazmul Khan Liton Md. Moshiur Rahman Swakkhar Shatabda Sajid Ahmed 8 42 0 21 Dec 2019
Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates Joseph Daws Clayton Webster 30 8 0 04 Dec 2019
Neural Contextual Bandits with UCB-based Exploration Dongruo Zhou Lihong Li Quanquan Gu 36 15 0 11 Nov 2019
Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs Z. Cai Jingshuang Chen Min Liu Xinyu Liu 10 88 0 05 Nov 2019
Growing axons: greedy learning of neural networks with application to function approximation Daria Fokina Ivan Oseledets 21 18 0 28 Oct 2019
Stochastic Feedforward Neural Networks: Universal Approximation Thomas Merkh Guido Montúfar 17 8 0 22 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 40 44 0 15 Oct 2019
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks Spencer Frei Yuan Cao Quanquan Gu ODL 9 31 0 07 Oct 2019
Bayesian Learning-Based Adaptive Control for Safety Critical Systems David D. Fan Jennifer Nguyen Rohan Thakker Nikhilesh Alatur Ali-akbar Agha-mohammadi Evangelos A. Theodorou BDL 28 84 0 05 Oct 2019
A Multi-level procedure for enhancing accuracy of machine learning algorithms K. Lye Siddhartha Mishra Roberto Molinaro 17 32 0 20 Sep 2019
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 25 33 0 09 Sep 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 29 161 0 25 Aug 2019
Space-time error estimates for deep neural network approximations for differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philipp Zimmermann 26 33 0 11 Aug 2019
Deep Network Approximation Characterized by Number of Neurons Zuowei Shen Haizhao Yang Shijun Zhang 20 182 0 13 Jun 2019
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization Yucheng Chen Matus Telgarsky Chao Zhang Bolton Bailey Daniel J. Hsu Jian-wei Peng GAN OT 16 17 0 08 Jun 2019
Nonlinear Approximation and (Deep) ReLU Networks Ingrid Daubechies Ronald A. DeVore S. Foucart Boris Hanin G. Petrova 22 138 0 05 May 2019
A neural network-based framework for financial model calibration Shuaiqiang Liu Anastasia Borovykh L. Grzelak C. Oosterlee 35 103 0 23 Apr 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning C. Chui Shao-Bo Lin Ding-Xuan Zhou 19 34 0 03 Apr 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs Gitta Kutyniok P. Petersen Mones Raslan R. Schneider 20 197 0 31 Mar 2019
Deep learning observables in computational fluid dynamics K. Lye Siddhartha Mishra Deep Ray OOD AI4CE 15 158 0 07 Mar 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions Belinda Tzen Maxim Raginsky DiffM 13 99 0 05 Mar 2019
Nonlinear Approximation via Compositions Zuowei Shen Haizhao Yang Shijun Zhang 26 92 0 26 Feb 2019
$Error bounds for approximations with deep ReLU neural networks in $W^{s,p}$ norms$ Error bounds for approximations with deep ReLU neural networks in $W^{s,p}$ norms Ingo Gühring Gitta Kutyniok P. Petersen 17 199 0 21 Feb 2019
Optimal Nonparametric Inference via Deep Neural Network Ruiqi Liu B. Boukai Zuofeng Shang 18 18 0 05 Feb 2019
The Oracle of DLphi Dominik Alfke W. Baines J. Blechschmidt Mauricio J. del Razo Sarmina Amnon Drory ... L. Thesing Philipp Trunschke Johannes von Lindheim David Weber Melanie Weber 39 0 0 17 Jan 2019
Deep Neural Network Approximation Theory Dennis Elbrächter Dmytro Perekrestenko Philipp Grohs Helmut Bölcskei 14 207 0 08 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 30 446 0 21 Nov 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 23 117 0 17 Oct 2018
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units Yixi Xu Tianlin Li MQ 28 12 0 03 Oct 2018
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients Arnulf Jentzen Diyora Salimova Timo Welti AI4CE 16 116 0 19 Sep 2018
Approximation and Estimation for High-Dimensional Deep Learning Networks Andrew R. Barron Jason M. Klusowski 27 59 0 10 Sep 2018
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philippe von Wurstemberger 11 167 0 07 Sep 2018
ResNet with one-neuron hidden layers is a Universal Approximator Hongzhou Lin Stefanie Jegelka 38 227 0 28 Jun 2018