Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence

30 May 2019

Papers citing "Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence"

25 / 25 papers shown

Title
Effective Regularization Through Loss-Function Metalearning Santiago Gonzalez Xin Qiu Risto Miikkulainen 105 0 0 02 Oct 2020
Extrapolation for Large-batch Training in Deep Learning Tao R. Lin Lingjing Kong Sebastian U. Stich Martin Jaggi 74 36 0 10 Jun 2020
Three Mechanisms of Weight Decay Regularization Guodong Zhang Chaoqi Wang Bowen Xu Roger C. Grosse 62 258 0 29 Oct 2018
Norm matters: efficient and accurate normalization schemes in deep networks Elad Hoffer Ron Banner Itay Golan Daniel Soudry OffRL 64 179 0 05 Mar 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 243 1,893 0 28 Dec 2017
Three Factors Influencing Minima in SGD Stanislaw Jastrzebski Zachary Kenton Devansh Arpit Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey 76 463 0 13 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 280 9,764 0 25 Oct 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 83 607 0 29 Jul 2017
L2 Regularization versus Batch and Weight Normalization Twan van Laarhoven 65 301 0 16 Jun 2017
Train longer, generalize better: closing the generalization gap in large batch training of neural networks Elad Hoffer Itay Hubara Daniel Soudry ODL 176 799 0 24 May 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 65 1,032 0 23 May 2017
Adaptive Regularization of Some Inverse Problems in Image Analysis Byung-Woo Hong JaKeoung Koo Martin Burger Stefano Soatto 46 9 0 09 May 2017
Sharp Minima Can Generalize For Deep Nets Laurent Dinh Razvan Pascanu Samy Bengio Yoshua Bengio ODL 116 772 0 15 Mar 2017
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 96 773 0 06 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 424 2,941 0 15 Sep 2016
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 333 8,130 0 13 Aug 2016
Recurrent Orthogonal Networks and Long-Memory Tasks Mikael Henaff Arthur Szlam Yann LeCun 64 133 0 22 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Adding Gradient Noise Improves Learning for Very Deep Networks Arvind Neelakantan Luke Vilnis Quoc V. Le Ilya Sutskever Lukasz Kaiser Karol Kurach James Martens AI4CE ODL 83 545 0 21 Nov 2015
Cyclical Learning Rates for Training Neural Networks L. Smith ODL 208 2,529 0 03 Jun 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 463 43,305 0 11 Feb 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,672 0 21 Dec 2014
New insights and perspectives on the natural gradient method James Martens ODL 73 624 0 03 Dec 2014
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 261 1,198 0 30 Nov 2014
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin Razvan Pascanu Çağlar Gülçehre Kyunghyun Cho Surya Ganguli Yoshua Bengio ODL 126 1,385 0 10 Jun 2014