Summary of Riemann Sum Optimization For Accurate Integrated Gradients Computation, by Swadesh Swain and Shree Singhi
Riemann Sum Optimization for Accurate Integrated Gradients Computation
by Swadesh Swain, Shree Singhi
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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, the authors propose a novel framework called RiemannOpt that aims to improve the accuracy of Integrated Gradients (IG), a widely used algorithm in deep learning. IG is used to attribute the outputs of neural networks to their input features, but its current implementation relies on inaccurate Riemann Sum approximations that introduce noise and false insights. The authors demonstrate how RiemannOpt minimizes these errors by optimizing sample point selection for the Riemann Sum, achieving up to 20% improvement in Insertion Scores. This framework is highly versatile, applicable not only to IG but also its derivatives like Blur IG and Guided IG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make deep learning models more transparent and understandable. The authors are trying to fix a problem with a popular tool called Integrated Gradients that helps us understand how neural networks work. Right now, this tool can give us wrong answers because it uses a simplified way of calculating things. The researchers developed a better way to do this calculation, which makes the answers more accurate and reliable. This new method is useful for many different types of deep learning models. |
Keywords
* Artificial intelligence * Deep learning
