The role of parameter Jacobians in the stability of network outputs
Read the original on arXiv Statistics ML →The paper investigates how parameter Jacobians influence the stability of network outputs within the framework of network dynamics, learning models, and neural tangent kernels (NTK). It demonstrates that linearized dynamics can be expressed as a semigroup of linear operators on Hilbert spaces, and provides explicit a priori perturbation bounds for fixed‑kernel linearizations in the NTK setting. The authors also offer refinements for task‑specific spaces, ergodic comparison estimates, spectral‑distribution conditions, and extensions to nonautonomous NTK evolutions, supported by worked examples.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.