Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.
arXiv preprint arXiv:2308.12108 , year =
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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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Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior
Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.
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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.