Understanding Text Generation Through Diffusion Models - From Theory to Implementation

Understanding Text Generation Through Diffusion Models - From Theory to Implementation

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Intro

1 of 11

1 of 11

Intro

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Understanding Text Generation Through Diffusion Models - From Theory to Implementation

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  1. 1 Intro
  2. 2 Modeling Probability Distributions for Generative AI
  3. 3 Problem #1: No Black Box
  4. 4 Solution #1: Train a Network to Approximate the Probability Mass Function
  5. 5 Problem #2: The Normalizing Constant, Z_theta, is Intractable
  6. 6 Solution #2: Autoregressive Modeling
  7. 7 Solution #3 Real Solution: Model Score, Not Probability Mass
  8. 8 Learning the Concrete Score Through Diffusion
  9. 9 Evaluation
  10. 10 So What?
  11. 11 Takeaways

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