Topic Models
author:
David Blei,
Computer Science Department, Princeton University
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| Slides | |
| 0:00 | Topic Models |
| 0:50 | The problem with information |
| 1:30 | Topic modeling |
| 3:06 | Discover topics from a corpus |
| 5:06 | Model the evolution of topics over time |
| 5:22 | Model connections between topics |
| 6:10 | Annotate images |
| 7:42 | Topic modeling topics (1) |
| 9:46 | Topic modeling topics (2) |
| 11:10 | Latent Dirichlet Allocation |
| 12:35 | Probabilistic modeling |
| 13:26 | Intuition behind LDA |
| 15:26 | Generative model |
| 22:42 | The posterior distribution |
| 23:42 | Graphical models (Aside) (1) |
| 25:14 | Graphical models (Aside) (2) |
| 26:14 | Latent Dirichlet allocation |
| 42:10 | The Dirichlet distribution (1) |
| 45:05 | The Dirichlet distribution (2) |
| 64:01 | Latent Dirichlet allocation (1) |
| 67:25 | Latent Dirichlet allocation (2) |
| 67:57 | Latent Dirichlet allocation (3) |
| 69:29 | Example inference (1) |
| 71:53 | Example inference (2) |
| 74:05 | Example inference (3) |
| 76:05 | Example inference (4) |
| 76:21 | Example inference (5) |
| 76:53 | Used to explore and browse document collections |
| 79:53 | Why does LDA "work"? |
| 81:00 | Generative model |
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