Measuring the Spectrum of Deepnet Hessians
We apply state-of-the-art tools in modern high-dimensional numerical linear algebra to approximate efficiently the spectrum of the Hessian of modern deepnets, with tens of millions of parameters, trained on real data. We decompose the Hessian into different components and study the dynamics with training and sample size of each term individually.
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