Summary of Bee: Metric-adapted Explanations Via Baseline Exploration-exploitation, by Oren Barkan et al.
BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation
by Oren Barkan, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed Baseline Exploration-Exploitation (BEE) method is a path-integration approach that introduces randomness to the integration process by modeling the baseline as a learned random tensor. This tensor is optimized through a contextual exploration-exploitation procedure to enhance performance on specific evaluation metrics. The BEE method generates a comprehensive set of explanation maps, allowing for the selection of the best-performing map for the given metric. Evaluations across various model architectures demonstrate the superior performance of BEE compared to state-of-the-art explanation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making explanations better and more consistent. Right now, there are many ways to evaluate how good an explanation is, and different baseline representations (ways to understand what’s missing) can affect these evaluations. To fix this, the authors propose a new method called Baseline Exploration-Exploitation (BEE). BEE uses randomness to create multiple explanations that account for different baselines, so you can choose the best one based on your goals. |
