Understanding Dropout as an Optimization Trick

26 June 2018

Papers citing "Understanding Dropout as an Optimization Trick"

26 / 26 papers shown

Title
Multi-Sample Dropout for Accelerated Training and Better Generalization H. Inoue 29 68 0 23 May 2019
Effective and Efficient Dropout for Deep Convolutional Neural Networks Shaofeng Cai Jinyang Gao Gang Chen Beng Chin Ooi Wei Wang Meihui Zhang BDL 51 53 0 06 Apr 2019
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift Xiang Li Shuo Chen Xiaolin Hu Jian Yang 48 309 0 16 Jan 2018
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 180 129,831 0 12 Jun 2017
YOLO9000: Better, Faster, Stronger Joseph Redmon Ali Farhadi VLM ObjD 107 15,535 0 25 Dec 2016
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 188 4,612 0 10 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 57 769 0 06 Nov 2016
FractalNet: Ultra-Deep Neural Networks without Residuals Gustav Larsson Michael Maire Gregory Shakhnarovich 57 934 0 24 May 2016
Noisy Activation Functions Çağlar Gülçehre Marcin Moczulski Misha Denil Yoshua Bengio 16 283 0 01 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 680 192,638 0 10 Dec 2015
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Chen-Yu Lee Patrick W. Gallagher Zhuowen Tu AI4CE 44 484 0 30 Sep 2015
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 84 1,500 0 08 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 349 9,233 0 06 Jun 2015
Visualizing and Understanding Recurrent Networks A. Karpathy Justin Johnson Li Fei-Fei HAI 64 1,100 0 05 Jun 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 151 43,154 0 11 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 142 149,474 0 22 Dec 2014
Efficient Object Localization Using Convolutional Networks Jonathan Tompson Ross Goroshin Arjun Jain Yann LeCun C. Bregler 3DH 61 1,346 0 16 Nov 2014
Recurrent Neural Network Regularization Wojciech Zaremba Ilya Sutskever Oriol Vinyals ODL 79 2,768 0 08 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 401 99,991 0 04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 579 39,383 0 01 Sep 2014
Deep Learning in Neural Networks: An Overview Jürgen Schmidhuber HAI 92 16,311 0 30 Apr 2014
Dropout improves Recurrent Neural Networks for Handwriting Recognition Vu Pham Théodore Bluche Christopher Kermorvant J. Louradour 61 566 0 05 Nov 2013
Improvements to deep convolutional neural networks for LVCSR Tara N. Sainath Brian Kingsbury Abdel-rahman Mohamed George E. Dahl G. Saon H. Soltau T. Beran Aleksandr Aravkin Bhuvana Ramabhadran 44 228 0 05 Sep 2013
Speech Recognition with Deep Recurrent Neural Networks Alex Graves Abdel-rahman Mohamed Geoffrey E. Hinton 49 8,503 0 22 Mar 2013
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 58 6,619 0 22 Dec 2012
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 294 7,650 0 03 Jul 2012