Summary of Flexible Tails For Normalizing Flows, by Tennessee Hickling and Dennis Prangle
Flexible Tails for Normalizing Flows
by Tennessee Hickling, Dennis Prangle
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers tackle the limitation of standard normalizing flows in representing distributions with heavy tails, which is crucial for density estimation and variational inference. By analyzing previous approaches like tail adaptive flow (TAF) methods, they identify an issue with optimizing neural networks under heavy-tailed inputs. To address this problem, the authors propose a new approach called tail transform flow (TTF), which uses a Gaussian base distribution and a final transformation layer to produce heavy tails. Experimental results show that TTF outperforms current methods, particularly when dealing with large-dimensional or heavy-tailed target distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to represent certain types of probability distributions using normalizing flows. Right now, there’s an issue with representing distributions that have really big tails (or ends). To solve this problem, the authors are trying something new called tail transform flow (TTF). TTF uses a simple base distribution and adds a special layer on top to make it possible to represent heavy-tailed distributions. The results show that TTF works better than other approaches when dealing with complex or extreme target distributions. |
Keywords
* Artificial intelligence * Density estimation * Inference * Probability
