Towards Understanding the Importance of Noise in Training Neural Networks

7 September 2019

Papers citing "Towards Understanding the Importance of Noise in Training Neural Networks"

9 / 9 papers shown

Title
Uncertainty Injection: A Deep Learning Method for Robust Optimization W. Cui Wei Yu UQCV OOD 27 6 0 23 Feb 2023
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably Tianyi Liu Yan Li Enlu Zhou Tuo Zhao 38 1 0 07 Feb 2022
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation Matilde Gargiani Andrea Zanelli Andrea Martinelli Tyler H. Summers John Lygeros 41 14 0 01 Feb 2022
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization Tianyi Liu Yan Li S. Wei Enlu Zhou T. Zhao 21 13 0 24 Feb 2021
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting Zeke Xie Fengxiang He Shaopeng Fu Issei Sato Dacheng Tao Masashi Sugiyama 21 60 0 12 Nov 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 44 79 0 17 Sep 2020
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy Boyi Liu Qi Cai Zhuoran Yang Zhaoran Wang 30 108 0 25 Jun 2019
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 312 2,896 0 15 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 186 1,186 0 30 Nov 2014