Summary of A Benchmark Suite For Evaluating Neural Mutual Information Estimators on Unstructured Datasets, by Kyungeun Lee and Wonjong Rhee
A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets
by Kyungeun Lee, Wonjong Rhee
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a comprehensive benchmark suite to evaluate neural Mutual Information (MI) estimators on unstructured datasets, focusing on images and texts. The benchmark suite aims to assess the reliability of these estimators by manipulating true MI values of real-world datasets using same-class sampling and a binary symmetric channel trick. The study investigates seven challenging scenarios, providing insights into the performance of neural MI estimators for unstructured datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well machine learning models can measure the connection between two things, like images or texts. Right now, we only have ways to test this with simple data sets that don’t look like real-life data. This new benchmark helps us see if our models work well on more complicated datasets. |
Keywords
* Artificial intelligence * Machine learning
