Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods

31 August 2021

Papers citing "Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods"

7 / 7 papers shown

Title
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing V. Monga Yuelong Li Yonina C. Eldar 75 1,012 0 22 Dec 2019
Nonlinear Approximation and (Deep) ReLU Networks Ingrid Daubechies Ronald A. DeVore S. Foucart Boris Hanin G. Petrova 41 139 0 05 May 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs Gitta Kutyniok P. Petersen Mones Raslan R. Schneider 51 199 0 31 Mar 2019
Deep Neural Networks Motivated by Partial Differential Equations Lars Ruthotto E. Haber AI4CE 64 488 0 12 Apr 2018
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Zhuowen Tu Kaiming He 430 10,281 0 16 Nov 2016
Deep Learning in Neural Networks: An Overview Jürgen Schmidhuber HAI 171 16,311 0 30 Apr 2014
Maxout Networks Ian Goodfellow David Warde-Farley M. Berk Mirza Aaron Courville Yoshua Bengio OOD 187 2,176 0 18 Feb 2013