EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
Deterministic annealing for clustering, compression, classification, regression, and related optimization problems
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EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.
Proposes distribution-level unsupervised feature discovery for LLMs by clustering continuations on semantic content and mechanistic attributions without target outputs.
Combined CMS result gives α^{Hττ} = 7 ± 16° for the CP mixing angle in H→ττ, consistent with SM expectation of 0 ± 14°.
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.
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Expected Free Energy-based Planning as Variational Inference
EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
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What Type of Inference is Active Inference?
EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.