By name: "ActiveDiffusion" description: "Expertise in integrating Active Inference and Diffusion Models for advanced cognitive system design and decision-making strategies." tags: ["active inference", "active-diffusion", "diffusion-models", "active-inference", "generative-ai", "probabilistic-modeling"]
Catechism for Towards Active Diffusion
Jakub Smékal, Daniel Friedman (2022) · Active Inference
Instructions
Use this skill when working with topics related to Active Diffusion, diffusion models, Active Inference, generative AI.
When applying this skill:
- Connect diffusion models with Active Inference principles
- Design collaborative research initiatives around emerging AI paradigms
Key Concepts
- Active Diffusion
- diffusion models
- Active Inference
- generative AI
- probabilistic modeling
- free energy minimization
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.
Key Findings
- Identifies significant similarities between Active Inference and Diffusion Models in their formulation and applications.
- Demonstrates the effectiveness of DMs in learning representations of dynamic environments.
- Reveals potential for bridging gaps between continuous and discrete time formulations in generative models.
- Highlights the role of latent state representations in enhancing sophisticated inference in high-dimensional spaces.
Prerequisites
- Probability theory and Bayesian inference basics
- Free Energy Principle and generative models
🎯 Consulting & Tutoring
Daniel Ari Friedman, PhD is available for AI Research Consulting and Tutoring related to this skill.
Related Skills
See BIBLIOGRAPHY.md for the complete publication catalog and related papers.