Summary of Remedi: Corrective Transformations For Improved Neural Entropy Estimation, by Viktor Nilsson et al.
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
by Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, Pierre Nyquist
First submitted to arxiv on: 8 Feb 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 This research paper introduces REMEDI, an efficient and accurate approach for estimating differential entropy, a fundamental information-theoretic quantity in machine learning. The method combines the minimization of cross-entropy for simple adaptive base models with the estimation of their deviation from the data density. This approach demonstrates improvement across various estimation tasks on both synthetic and natural datasets. The framework can be extended to information-theoretic supervised learning models, including the Information Bottleneck approach, achieving better accuracy compared to existing methods. Additionally, REMEDI is connected to generative modeling using rejection sampling and Langevin dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new method for estimating important information-theoretic quantities in machine learning. As data and models become more complex, it’s harder to accurately calculate these numbers. The researchers created a way called REMEDI that works well across many different tasks and datasets. It’s especially good at calculating entropy, which is a key part of some machine learning techniques like the Information Bottleneck approach. REMEDI can even be used for generative modeling, making new kinds of artificial data. |
Keywords
* Artificial intelligence * Cross entropy * Machine learning * Supervised
