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Representation Learning of Grounded Language and Knowledge - With and Without End-to-End Learning
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- 1 Intro
- 2 Intelligent Communication
- 3 Types of Knowledge
- 4 End-to-End Learning
- 5 Cooking Instructions
- 6 Biology Wet Lab Instructions
- 7 How to Change Engine Oil
- 8 Unique Challenges of Procedural Language
- 9 Action graphs
- 10 Action graph for blueberry muffins
- 11 Unsupervised Learning
- 12 Knowledge in the Model
- 13 Learned cooking knowledge
- 14 How to generate recipes
- 15 Task Definition
- 16 Recipe generation as machine translation?
- 17 Encode title - decode recipe
- 18 Recipe generation vs machine translation
- 19 Let's make salsa!
- 20 Checklist is probabilistic
- 21 Hidden state classifier is soft
- 22 Interpolation
- 23 Choose ingredient via attention
- 24 Attention-generated embeddings
- 25 Baselines
- 26 Neural Recipe Example 1
- 27 Example: skillet chicken rice
- 28 Example: chocolate covered potato chips
- 29 Neural checklist model for dialogue generation
- 30 Hotel domain
- 31 What's missing in the end-to-end...
- 32 Dynamic ??? Networks
- 33 Representation: Verb Physics Frames
- 34 Reverse Engineering Commonsense Knowledge!
- 35 Conclusion (as of today)
- 36 Intersect FSA with RNN Language Model
