Approximation Properties of Deep ReLU CNNs

1 September 2021

Papers citing "Approximation Properties of Deep ReLU CNNs"

26 / 26 papers shown

Title
Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces Yuwen Li Guozhi Zhang 58 1 0 20 Jan 2025
An Interpretive Constrained Linear Model for ResNet and MgNet Juncai He Jinchao Xu Lian Zhang Jianqing Zhu 34 18 0 14 Dec 2021
Characterization of the Variation Spaces Corresponding to Shallow Neural Networks Jonathan W. Siegel Jinchao Xu 32 43 0 28 Jun 2021
Universal Consistency of Deep Convolutional Neural Networks Shao-Bo Lin Kaidong Wang Yao Wang Ding-Xuan Zhou 29 22 0 23 Jun 2021
ReLU Deep Neural Networks from the Hierarchical Basis Perspective Juncai He Lin Li Jinchao Xu AI4CE 38 30 0 10 May 2021
Universal Approximation Theorem for Equivariant Maps by Group CNNs Wataru Kumagai Akiyoshi Sannai 69 13 0 27 Dec 2020
Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model Michael Kohler S. Langer 29 17 0 27 Nov 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 21 42,038 0 03 Dec 2019
Constrained Linear Data-feature Mapping for Image Classification Juncai He Yuyan Chen Lian Zhang Jinchao Xu 19 2 0 23 Nov 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 27 17,950 0 28 May 2019
Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks Kenta Oono Taiji Suzuki 42 58 0 24 Mar 2019
Nonlinear Approximation via Compositions Zuowei Shen Haizhao Yang Shijun Zhang 33 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 35 200 0 21 Feb 2019
Equivalence of approximation by convolutional neural networks and fully-connected networks P. Petersen Felix Voigtländer 20 78 0 04 Sep 2018
Universality of Deep Convolutional Neural Networks Ding-Xuan Zhou HAI PINN 44 513 0 28 May 2018
The Expressive Power of Neural Networks: A View from the Width Zhou Lu Hongming Pu Feicheng Wang Zhiqiang Hu Liwei Wang 46 886 0 08 Sep 2017
Understanding Deep Neural Networks with Rectified Linear Units R. Arora A. Basu Poorya Mianjy Anirbit Mukherjee PINN 112 640 0 04 Nov 2016
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review T. Poggio H. Mhaskar Lorenzo Rosasco Brando Miranda Q. Liao 48 575 0 02 Nov 2016
Error bounds for approximations with deep ReLU networks Dmitry Yarotsky 56 1,226 0 03 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 437 36,599 0 25 Aug 2016
$Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $ \ell^1 $ and $ \ell^0 $ Controls$ Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $\ell^1$ and $\ell^0$ Controls Jason M. Klusowski Andrew R. Barron 144 142 0 26 Jul 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 185 10,149 0 16 Mar 2016
Benefits of depth in neural networks Matus Telgarsky 220 605 0 14 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 673 192,638 0 10 Dec 2015
Breaking the Curse of Dimensionality with Convex Neural Networks Francis R. Bach 34 701 0 30 Dec 2014
On the Number of Linear Regions of Deep Neural Networks Guido Montúfar Razvan Pascanu Kyunghyun Cho Yoshua Bengio 43 1,249 0 08 Feb 2014