Dead directions recover Watanabe's RLCT contribution and triple (λ, m, ν) from directional Fisher curvature decay rates in original parameter space for singular models, extended via K-FAC to networks and gauge-equivariant optimizers.
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Susceptibilities defined via posterior covariances serve as the Jacobian for mapping data distributions to structural coordinates in Bayesian learning, with pseudo-inverse solving for desired structural changes.
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Dead Directions: Geometric Singular Learning
Dead directions recover Watanabe's RLCT contribution and triple (λ, m, ν) from directional Fisher curvature decay rates in original parameter space for singular models, extended via K-FAC to networks and gauge-equivariant optimizers.
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Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning
Susceptibilities defined via posterior covariances serve as the Jacobian for mapping data distributions to structural coordinates in Bayesian learning, with pseudo-inverse solving for desired structural changes.