The Evolution of the Interplay Between Input Distributions and Linear Regions in Networks

28 October 2023

Papers citing "The Evolution of the Interplay Between Input Distributions and Linear Regions in Networks"

22 / 22 papers shown

Title
On the Number of Regions of Piecewise Linear Neural Networks Alexis Goujon Arian Etemadi M. Unser 75 16 0 17 Jun 2022
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks Hao Chen Yu Wang Huan Xiong GNN 49 6 0 01 Jun 2022
GhostNets on Heterogeneous Devices via Cheap Operations Kai Han Yunhe Wang Chang Xu Jianyuan Guo Chunjing Xu Enhua Wu Qi Tian 33 106 0 10 Jan 2022
Using activation histograms to bound the number of affine regions in ReLU feed-forward neural networks Peter Hinz 34 6 0 31 Mar 2021
Deep ReLU Networks Have Surprisingly Few Activation Patterns Boris Hanin David Rolnick 81 227 0 03 Jun 2019
Complexity of Linear Regions in Deep Networks Boris Hanin David Rolnick 44 232 0 25 Jan 2019
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions Hongyang Gao Zhengyang Wang Shuiwang Ji 34 70 0 05 Sep 2018
Sensitivity and Generalization in Neural Networks: an Empirical Study Roman Novak Yasaman Bahri Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein AAML 93 439 0 23 Feb 2018
Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients? Boris Hanin 62 254 0 11 Jan 2018
Bounding and Counting Linear Regions of Deep Neural Networks Thiago Serra Christian Tjandraatmadja Srikumar Ramalingam MLT 65 250 0 06 Nov 2017
Optimal approximation of piecewise smooth functions using deep ReLU neural networks P. Petersen Felix Voigtländer 223 475 0 15 Sep 2017
Exploring Generalization in Deep Learning Behnam Neyshabur Srinadh Bhojanapalli David A. McAllester Nathan Srebro FAtt 148 1,256 0 27 Jun 2017
The power of deeper networks for expressing natural functions David Rolnick Max Tegmark 156 174 0 16 May 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 1.1K 20,837 0 17 Apr 2017
Understanding Deep Neural Networks with Rectified Linear Units R. Arora A. Basu Poorya Mianjy Anirbit Mukherjee PINN 146 641 0 04 Nov 2016
Error bounds for approximations with deep ReLU networks Dmitry Yarotsky 195 1,227 0 03 Oct 2016
Why does deep and cheap learning work so well? Henry W. Lin Max Tegmark David Rolnick 79 607 0 29 Aug 2016
On the Expressive Power of Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 61 788 0 16 Jun 2016
The Power of Depth for Feedforward Neural Networks Ronen Eldan Ohad Shamir 216 732 0 12 Dec 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 323 18,613 0 06 Feb 2015
On the Number of Linear Regions of Deep Neural Networks Guido Montúfar Razvan Pascanu Kyunghyun Cho Yoshua Bengio 88 1,254 0 08 Feb 2014
On the number of response regions of deep feed forward networks with piece-wise linear activations Razvan Pascanu Guido Montúfar Yoshua Bengio FAtt 117 257 0 20 Dec 2013