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Neural networks can have redundant parametrizations, making their evolution dynamics ill-conditioned. Dirac-Frenkel dynamics with inertia can help. It's tested in simulation only, so we don't know if it holds up in real-world problems.
“arXiv:2606.24769v2 Announce Type: replace-cross Abstract: Even when Dirac-Frenkel dynamics determine a well-defined evolution in function space, the corresponding parameter dynamics can be non-unique or ill-conditioned for redundant nonline…”
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Dirac-Frenkel dynamics, arXiv
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LAST OBSERVED
Aug 8, 2026