Summary of Explanations That Reveal All Through the Definition Of Encoding, by Aahlad Puli et al.
Explanations that reveal all through the definition of encoding
by Aahlad Puli, Nhi Nguyen, Rajesh Ranganath
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 This paper explores the concept of feature attributions in machine learning, specifically focusing on encoding explanations. The authors propose a definition of encoding that identifies the extra predictive power and demonstrate its applicability through various examples. Additionally, they develop STRIPE-X, an evaluation method that correctly ranks non-encoding explanations above encoding ones. The study concludes with empirical results showing the effectiveness of the proposed approach in sentiment analysis tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how machine learning models make predictions. It’s like trying to figure out what makes a good recipe work. The researchers are looking at special kinds of explanations that help us understand why a model made a certain prediction. They came up with a new way to define these explanations and showed it works on different examples. Then, they created a tool to measure how good these explanations are. The paper also tests this approach on a specific task called sentiment analysis. |
Keywords
* Artificial intelligence * Machine learning
