Introduction To Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
Source
- Raw Markdown: paper_latent-variable-energy-based-models-2023.md
- PDF: paper_latent-variable-energy-based-models-2023.pdf
Core Claim
These lecture notes explain latent-variable energy-based models and H-JEPA as core concepts behind LeCun’s autonomous intelligence proposal.
Key Contributions
- Introduces limitations of supervised learning and reinforcement learning for human-like sample efficiency.
- Explains energy-based and latent-variable models.
- Contrasts contrastive and regularized EBM training.
- Connects JEPA and H-JEPA to hierarchical world modeling and planning under uncertainty.
Method Notes
LVEBM is a pedagogical bridge between APTAMI, Energy-Based Models, and JEPA.
Evidence And Results
The source is explanatory rather than a benchmark paper. Its wiki value is conceptual clarification and links between EBMs, latent variables, and H-JEPA.
Limitations
It inherits the speculative scope of the underlying autonomous-intelligence proposal and does not provide a single new empirical system.
Links Into The Wiki
Open Questions
- Which EBM training method is most compatible with modern large-scale JEPA systems?
- How should latent variables be represented in practical world models?