A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
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Cognitive Flexibility is a new representation-level operator for Bayesian filters that dynamically selects latent structures via predictive scores to reduce inconsistency under mismatch while preserving the recursion and exhibiting descent and finite-switching properties.
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Physics-informed neural particle flow for the Bayesian update step
A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
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Cognitive Flexibility as a Latent Structural Operator for Bayesian State Estimation
Cognitive Flexibility is a new representation-level operator for Bayesian filters that dynamically selects latent structures via predictive scores to reduce inconsistency under mismatch while preserving the recursion and exhibiting descent and finite-switching properties.