The Laplace-Fisher Gate Identity supplies the variance-optimal matrix blending coefficients for Tweedie and target-score estimators under an OU diffusion, enabling improved finite-reference score estimation and posterior density surrogates.
On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables.Biometrika, 12(1/2):134–139
3 Pith papers cite this work. Polarity classification is still indexing.
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In linear recurrent models, infinite-width signal propagation remains accurate only for depths t much smaller than sqrt(width n), with a critical regime at t ~ c sqrt(n) where finite-width effects emerge and dominate for larger t.
The error-gated Hebbian rule for PCA arises exactly as a frame coefficient when Oja's subspace rule is expanded in the space of symmetric matrices under Gaussian inputs.
citing papers explorer
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Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
The Laplace-Fisher Gate Identity supplies the variance-optimal matrix blending coefficients for Tweedie and target-score estimators under an OU diffusion, enabling improved finite-reference score estimation and posterior density surrogates.
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How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences
In linear recurrent models, infinite-width signal propagation remains accurate only for depths t much smaller than sqrt(width n), with a critical regime at t ~ c sqrt(n) where finite-width effects emerge and dominate for larger t.
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Frame Theoretical Derivation of Three Factor Learning Rule for Oja's Subspace Rule
The error-gated Hebbian rule for PCA arises exactly as a frame coefficient when Oja's subspace rule is expanded in the space of symmetric matrices under Gaussian inputs.