Summary of Building Expressive and Tractable Probabilistic Generative Models: a Review, by Sahil Sidheekh et al.
Building Expressive and Tractable Probabilistic Generative Models: A Review
by Sahil Sidheekh, Sriraam Natarajan
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys the advancements in tractable probabilistic generative modeling, with a focus on Probabilistic Circuits (PCs). The authors provide a unified perspective on the trade-offs between expressivity and tractability, highlighting design principles and algorithmic extensions that enable building expressive and efficient PCs. They also discuss recent efforts to build deep and hybrid PCs by fusing notions from deep neural models. The paper outlines challenges and open questions guiding future research in this evolving field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make computers generate things like pictures or music, but in a way that’s efficient and easy to understand. They’re interested in something called Probabilistic Circuits (PCs), which are like special computers that can create lots of different possibilities. The authors explain how PCs work and what makes them good or bad for certain tasks. They also talk about new ways people are using PCs, like combining them with deep learning models. |
Keywords
* Artificial intelligence * Deep learning
