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New inertial Dirac-Frenkel dynamics improve parameter stability in neural networks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an inertial formulation of Dirac-Frenkel dynamics to address issues with non-unique or ill-conditioned parameter dynamics in redundant nonlinear parametrizations like neural networks. This new method incorporates inertia, allowing past trajectory information to inform parameter velocity directions that are weakly constrained, while strongly constrained directions continue to follow the original dynamics. The inertial formulation is proven to yield well-posed parameter dynamics and provides a posteriori error bounds, demonstrating increased robustness in numerical experiments. AI

IMPACT This research could lead to more stable and robust training of complex neural network models by improving parameter dynamics.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new mathematical dynamics formulation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New inertial Dirac-Frenkel dynamics improve parameter stability in neural networks

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Matteo Raviola, Benjamin Peherstorfer ·

    Dirac-Frenkel dynamics with inertia for nonlinearly parametrized solutions of evolution problems

    arXiv:2606.24769v1 Announce Type: 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 nonlinear parametrizations such as neural networks or m…

  2. arXiv cs.LG TIER_1 English(EN) · Benjamin Peherstorfer ·

    Dirac-Frenkel dynamics with inertia for nonlinearly parametrized solutions of evolution problems

    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 nonlinear parametrizations such as neural networks or mixture models. We propose to add inertia to the Di…

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