Representation Learning of Grounded Language and Knowledge - With and Without End-to-End Learning

Representation Learning of Grounded Language and Knowledge - With and Without End-to-End Learning

Simons Institute via YouTube Direct link

Representation: Verb Physics Frames

33 of 36

33 of 36

Representation: Verb Physics Frames

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Representation Learning of Grounded Language and Knowledge - With and Without End-to-End Learning

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  1. 1 Intro
  2. 2 Intelligent Communication
  3. 3 Types of Knowledge
  4. 4 End-to-End Learning
  5. 5 Cooking Instructions
  6. 6 Biology Wet Lab Instructions
  7. 7 How to Change Engine Oil
  8. 8 Unique Challenges of Procedural Language
  9. 9 Action graphs
  10. 10 Action graph for blueberry muffins
  11. 11 Unsupervised Learning
  12. 12 Knowledge in the Model
  13. 13 Learned cooking knowledge
  14. 14 How to generate recipes
  15. 15 Task Definition
  16. 16 Recipe generation as machine translation?
  17. 17 Encode title - decode recipe
  18. 18 Recipe generation vs machine translation
  19. 19 Let's make salsa!
  20. 20 Checklist is probabilistic
  21. 21 Hidden state classifier is soft
  22. 22 Interpolation
  23. 23 Choose ingredient via attention
  24. 24 Attention-generated embeddings
  25. 25 Baselines
  26. 26 Neural Recipe Example 1
  27. 27 Example: skillet chicken rice
  28. 28 Example: chocolate covered potato chips
  29. 29 Neural checklist model for dialogue generation
  30. 30 Hotel domain
  31. 31 What's missing in the end-to-end...
  32. 32 Dynamic ??? Networks
  33. 33 Representation: Verb Physics Frames
  34. 34 Reverse Engineering Commonsense Knowledge!
  35. 35 Conclusion (as of today)
  36. 36 Intersect FSA with RNN Language Model

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