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Deep neural networks (DNNs) are powerful tools for compressing and distilling information.
The Hartree-Fock Method for Atoms: A Numerical Approach
C.F. Fischer and D.R. Hartree · 1977
Earlier work this paper cites.
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Normalization effects on shallow neural networks and related asymptotic expansions
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Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
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