Non-Gaussianity of Stochastic Gradient Noise

2019/10/21 by Abhishek Panigrahi, Panigrahi, Abhishek, Raghav Somani +5 · 1 citation
Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.1910.09626

openalex publication_date 2019/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training? This question has attracted much attention. In this paper, we study the distribution of the Stochastic Gradient Noise (SGN) vectors during the training. We observe that for batch sizes 256 and above, the distribution is best described as Gaussian at-least in the early phases of training. This holds across data-sets, architectures, and other choices.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Stochastic Approximation Method
• ImageNet classification with deep convolutional neural networks
• An analysis of variance test for normality (complete samples)
• Non-convex learning via Stochastic Gradient Langevin Dynamics: a\n nonasymptotic analysis
• Three Factors Influencing Minima in SGD
• On the diffusion approximation of nonconvex stochastic gradient descent
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Quantifying Information Disclosure During Gradient Descent Using Gradient Uniqueness
• On the Noisy Gradient Descent that Generalizes as SGD

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