How to Represent Part-Whole Hierarchies in a Neural Network - Geoff Hinton's Paper Explained

How to Represent Part-Whole Hierarchies in a Neural Network - Geoff Hinton's Paper Explained

Yannic Kilcher via YouTube Direct link

- Top-Down and Bottom-Up communication

5 of 13

5 of 13

- Top-Down and Bottom-Up communication

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How to Represent Part-Whole Hierarchies in a Neural Network - Geoff Hinton's Paper Explained

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  1. 1 - Intro & Overview
  2. 2 - Object Recognition as Parse Trees
  3. 3 - Capsule Networks
  4. 4 - GLOM Architecture Overview
  5. 5 - Top-Down and Bottom-Up communication
  6. 6 - Emergence of Islands
  7. 7 - Cross-Column Attention Mechanism
  8. 8 - My Improvements for the Attention Mechanism
  9. 9 - Some Design Decisions
  10. 10 - Training GLOM as a Denoising Autoencoder & Contrastive Learning
  11. 11 - Coordinate Transformations & Representing Uncertainty
  12. 12 - How GLOM handles Video
  13. 13 - Conclusion & Comments

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