Summary of Mutual Information Estimation Via Normalizing Flows, by Ivan Butakov et al.
Mutual Information Estimation via Normalizing Flows
by Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk, Anna Neopryatnaya, Alexey Frolov
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Machine Learning (stat.ML)
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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 presents a novel approach to estimating mutual information (MI) using normalizing flows, which map original data to a target distribution where MI is easier to estimate. The authors also explore target distributions with known closed-form expressions for MI, providing theoretical guarantees for their method’s accuracy. They demonstrate the practical advantages of this approach through experiments on high-dimensional data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier to calculate how related two datasets are (mutual information). They found a way to do this using something called normalizing flows, which helps match the data to another distribution where it’s simpler to figure out the relationship. The authors also tested their method on big datasets and showed that it works well. |
