Summary of An Information Theoretic Limit to Data Amplification, by S. J. Watts and L. Crow
An information theoretic limit to data amplification
by S. J. Watts, L. Crow
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); High Energy Physics – Experiment (hep-ex); Data Analysis, Statistics and Probability (physics.data-an)
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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 Generative Adversarial Networks (GANs) are used to create data for science analysis, reducing computing time. A gain factor (G) greater than one is achieved through “data amplification,” violating the principle that information cannot be gained without cost. This study explores conditions on underlying and reconstructed probability distributions ensuring this bound. The resolution of variables in amplified data is not improved, but increased sample size can enhance statistical significance. A mathematical bound depends solely on generated and training events. GAN-generated data from literature confirms this bound through computer simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists use special computers to create fake data to help with their research. This helps them work faster and get better results. Sometimes, these computers can even make more fake data than the real thing! But that’s okay because it doesn’t change what we’re trying to learn. The study looks at how this process works and finds a rule that says how much more fake data they can make while keeping things fair. |
Keywords
» Artificial intelligence » Gan » Probability
