Diagonal EP under variance-profile Gaussian matrices produces Gaussian-process dynamics with profile-dependent memory instead of conventional scalar state evolution.
ηr 0θ1−η1 rX p=1 ηr−p 0 α1/2Wp+α JX j=1 µjℓ⊤ j,p !!⊤ ηs 0θ1−η1 sX q=1 ηs−q 0 α1/2Wq+α JX k=1 µkℓ⊤ k,q !!# = α−1 d E
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Decoupled descent enforces asymptotic tracking of test error by training error in Gaussian mixture models through bias cancellation via approximate message passing, enabling full data utilization.
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Gaussian-Process Dynamics of Diagonal Expectation Propagation under Variance-Profile Gaussian Measurements
Diagonal EP under variance-profile Gaussian matrices produces Gaussian-process dynamics with profile-dependent memory instead of conventional scalar state evolution.
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Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Decoupled descent enforces asymptotic tracking of test error by training error in Gaussian mixture models through bias cancellation via approximate message passing, enabling full data utilization.