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New research explores parameter Jacobians for neural network stability

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This paper explores the stability of network outputs within the framework of learning models and neural tangent kernels (NTK). It demonstrates that linearized dynamics naturally lead to a semigroup formulation, presenting time-dynamics through special semigroups of linear operators on Hilbert spaces. The research provides new, explicit a priori perturbation-bound results, including finite-time perturbation estimates for NTK constructions and extensions to nonautonomous NTK evolutions. AI

RANK_REASON The item is an academic paper published on arXiv detailing mathematical research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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New research explores parameter Jacobians for neural network stability

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The item is an academic paper published on arXiv detailing mathematical research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Halyun Jeong, Palle E. T. Jorgensen, Hyun-Kyoung Kwon, Myung-Sin Song, James Tian ·

    The role of parameter Jacobians in the stability of network outputs

    arXiv:2608.27748v1 Announce Type: cross Abstract: In the framework of network dynamics, learning models, and neural tangent kernels (NTK), we show that the corresponding linearized dynamics leads naturally to a semigroup formulation. More precisely, in our analysis of input/outpu…

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