Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation
A neural network's layers can be treated as time steps of a state-space model, turning Bayesian training into a smoothing problem: a forward pass propagates Gaussian moments through the network, and a backward Rauch--Tung--Striebel pass updates the weight posteriors in closed form. Such methods learn from each observat...
Oren Wright, Hao-Ming Jing, Qiao-An Shen et al.
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