SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep
Learning

SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning

21 May 2018

Yiran Chen

Papers citing "SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning"

8 / 8 papers shown

Title
Layer-wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning Sunwoo Lee 109 0 0 18 Mar 2025
Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks Yequan Zhao Xinling Yu Xian Xiao Z. Chen Z. Liu G. Kurczveil R. Beausoleil S. Liu Z. Zhang 56 0 0 17 Feb 2025
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization Xiaotian Han Tong Zhao Yozen Liu Xia Hu Neil Shah GNN 55 36 0 30 Sep 2022
Towards Understanding Sharpness-Aware Minimization Maksym Andriushchenko Nicolas Flammarion AAML 26 133 0 13 Jun 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 20 8 0 18 Feb 2022
Activated Gradients for Deep Neural Networks Mei Liu Liangming Chen Xiaohao Du Long Jin Mingsheng Shang ODL AI4CE 19 135 0 09 Jul 2021
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 281 2,889 0 15 Sep 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015