Summary of Constructing Sensible Baselines For Integrated Gradients, by Jai Bardhan et al.
Constructing sensible baselines for Integrated Gradients
by Jai Bardhan, Cyrin Neeraj, Mihir Rawat, Subhadip Mitra
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Physics – Experiment (hep-ex)
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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 explore ways to understand machine learning models, which have become increasingly popular in scientific applications. The authors focus on integrated gradients (IGs) as a method for interpreting these “black box” models. They demonstrate how IGs can be applied by designing different baselines and conducting an example case study in particle physics. The results show that using an averaged baseline sampled from the background events provides more reasonable feature attributions compared to other approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand machine learning models, which are used in many scientific projects. Scientists want to know what these models are doing behind the scenes. Researchers found a way to do this by using something called integrated gradients (IGs). They tested it on an example project about tiny particles and discovered that one method works better than others. |
Keywords
» Artificial intelligence » Machine learning
