Summary of Fast Explanations Via Policy Gradient-optimized Explainer, by Deng Pan et al.
Fast Explanations via Policy Gradient-Optimized Explainer
by Deng Pan, Nuno Moniz, Nitesh Chawla
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackle the issue of providing efficient model explanations for real-world applications. Traditional methods often rely on extensive queries or expert knowledge, which hinders their adoption. To address these limitations, they introduce Fast Explanation (FEX), a novel framework that represents attribution-based explanations as probability distributions, optimized using policy gradient method. FEX offers a scalable and robust solution for real-time model explanations, bridging the gap between efficiency and applicability. The authors demonstrate its effectiveness on image and text classification tasks, achieving over 97% reduction in inference time and 70% decrease in memory usage compared to traditional methods while maintaining high-quality explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem: making AI models explain themselves quickly and easily. Right now, most methods are slow or require expert knowledge, which makes them hard to use in real-life situations. To fix this, the researchers created a new way to understand how models work called Fast Explanation (FEX). FEX is fast, efficient, and works well with big datasets. It’s like having a special tool that helps you quickly figure out why an AI model made a certain decision. |
Keywords
» Artificial intelligence » Inference » Probability » Text classification
