Activediffusion

• 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.