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Renormalising Generative Models paper clarifies active inference framework

By PulseAugur Editorial · [1 sources] ·

This paper presents a derivation-oriented account of Renormalising Generative Models (RGMs) for active inference, aiming to make the framework more accessible. RGMs address the challenge of scaling active inference to complex domains by composing discrete generative models across different scales. The work clarifies the RGM hierarchy, belief and action updates, and information flow between levels, providing an open and verified implementation to lower barriers for researchers. AI

IMPACT This paper aims to make active inference more accessible for researchers, potentially accelerating development and evaluation on machine learning benchmarks.

RANK_REASON The item is a research paper detailing a theoretical framework and its implementation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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Renormalising Generative Models paper clarifies active inference framework

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The item is a research paper detailing a theoretical framework and its implementation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification

    Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult. Renormalising generative models (RGMs) address this challenge by composing discrete generative mo…

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