Effective and Efficient Dropout for Deep Convolutional Neural Networks

Effective and Efficient Dropout for Deep Convolutional Neural Networks

6 April 2019

Papers citing "Effective and Efficient Dropout for Deep Convolutional Neural Networks"

12 / 12 papers shown

Title
How to Use Dropout Correctly on Residual Networks with Batch Normalization Bum Jun Kim Hyeyeon Choi Hyeonah Jang Donggeon Lee Sang Woo Kim 22 7 0 13 Feb 2023
Evaluating CNN with Oscillatory Activation Function Jeevanshi Sharma 13 1 0 13 Nov 2022
Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results T. Ridnik Hussam Lawen Emanuel Ben-Baruch Asaf Noy 38 11 0 07 Apr 2022
A Survey on Dropout Methods and Experimental Verification in Recommendation Y. Li Weizhi Ma C. L. Philip Chen M. Zhang Yiqun Liu Shaoping Ma Yue Yang 33 9 0 05 Apr 2022
Decoding ECoG signal into 3D hand translation using deep learning Maciej Śliwowski Matthieu Martin Antoine Souloumiac P. Blanchart T. Aksenova 20 27 0 05 Oct 2021
Productivity, Portability, Performance: Data-Centric Python Yiheng Wang Yao Zhang Yanzhang Wang Yan Wan Jiao Wang Zhongyuan Wu Yuhao Yang Bowen She 52 94 0 01 Jul 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 32 2 0 04 Jan 2021
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 31 79 0 17 Sep 2020
Survey of Dropout Methods for Deep Neural Networks Alex Labach Hojjat Salehinejad S. Valaee 18 149 0 25 Apr 2019
Understanding Dropout as an Optimization Trick Sangchul Hahn Heeyoul Choi ODL 13 34 0 26 Jun 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,634 0 03 Jul 2012