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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...
It is found that behavioral fidelity is not uniform across metrics: statistical priors bring activity and sequence distributions closest to real behavior, yet over-fragment routines and suppress within-person variability.
SemanticSlider3D is presented, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training and supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.
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