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The Ethereal
Non-Gaussianity of Stochastic Gradient Noise
October 21, 2019 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli
arXiv ID
1910.09626
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
57
Venue
arXiv.org
Last Checked
5 months ago
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.
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