Catechism for Towards Active Diffusion

Catalog Row67

Citation KeyFriedman2022CatechismTowardsActiveDiffusion067

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Overview

Extracted from the local paper documentation when available.

This paper explores the connections between Active Inference (ActInf) and Diffusion Models (DM) in the context of cognitive sciences and artificial intelligence. It aims to characterize mathematical formalisms and computational applications of these models to enhance decision-making and planning strategies under the Free Energy Principle.

Active Diffusiondiffusion modelsActive Inferencegenerative AIprobabilistic modelingfree energy minimization

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts

Establishes theoretical connections between Active Inference and Diffusion Models.
Develops open-source repositories to demonstrate concordances among ActInf, DM, and related methods.
Integrates diffusion models within the active inference framework for dynamic environment representation.
Explores the implications of Active Diffusion in cognitive ecosystems and decentralized science.

Methods / Techniques

Conducts a thorough literature review on Latent Diffusion Models and belief propagation in Active Inference.
Utilizes benchmarks on standard datasets and formats for empirical validation.
Employs the cadCAD package for developing frameworks in cognitive ecosystems design.
Investigates the application of individual components within LDM architectures in the action-perception loop.

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2022. Catechism for Towards Active Diffusion. Zenodo.

Primary source Documentation BibTeX