Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification
This paper provides a self-contained, derivation-oriented account of RGMs, clarifying the theory and separating it from its original implementation context, and makes RGMs more transparent, auditable, and reproducible, providing a foundation for future quantitative evaluation and development on machine-learning benchma...
Karim Zaghw, A. Pashea, M. Pritsch et al.
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