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The Ethereal
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
November 22, 2016 ยท Declared Dead ยท + Add venue
"No code URL or promise found in abstract"
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Authors
Levent Sagun, Leon Bottou, Yann LeCun
arXiv ID
1611.07476
Category
cs.LG: Machine Learning
Citations
264
Last Checked
3 months ago
Abstract
We look at the eigenvalues of the Hessian of a loss function before and after training. The eigenvalue distribution is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. We present empirical evidence for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data.
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