Norm-Preservation: Why Residual Networks Can Become Extremely Deep?

Norm-Preservation: Why Residual Networks Can Become Extremely Deep?

18 May 2018

Alireza Zaeemzadeh

Nazanin Rahnavard

Papers citing "Norm-Preservation: Why Residual Networks Can Become Extremely Deep?"

7 / 7 papers shown

Title
Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration D. Wolf Alexander Braun Markus Ulrich 99 0 0 18 Dec 2024
Learning Temporal Resolution in Spectrogram for Audio Classification Haohe Liu Xubo Liu Qiuqiang Kong Wenwu Wang Mark D. Plumbley 34 7 0 04 Oct 2022
Augmenting Deep Classifiers with Polynomial Neural Networks Grigorios G. Chrysos Markos Georgopoulos Jiankang Deng Jean Kossaifi Yannis Panagakis Anima Anandkumar 24 18 0 16 Apr 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 40 2 0 04 Jan 2021
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 44 79 0 17 Sep 2020
GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation Jiawei Zhang Lin Meng 21 52 0 12 Sep 2019
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization Peter L. Bartlett S. Evans Philip M. Long 76 31 0 13 Apr 2018