Summary of Prospector Heads: Generalized Feature Attribution For Large Models & Data, by Gautam Machiraju et al.
Prospector Heads: Generalized Feature Attribution for Large Models & Data
by Gautam Machiraju, Alexander Derry, Arjun Desai, Neel Guha, Amir-Hossein Karimi, James Zou, Russ Altman, Christopher Ré, Parag Mallick
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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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 paper introduces “prospector heads,” an efficient and interpretable method for feature attribution that can be applied to any encoder and data modality. Unlike existing methods, which rely on explaining the predictions of end-to-end classifiers, prospector heads provide precise feature localization and can handle small sample sizes and high-dimensional datasets. The approach is demonstrated through experiments on sequences (text), images (pathology), and graphs (protein structures), outperforming baseline attribution methods by up to 26.3 points in mean localization AUPRC. Prospector heads enable improved interpretation and discovery of class-specific patterns in input data, providing a framework for improving trust and transparency for ML models in complex domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prospectors are a new way to understand how machine learning models work. They help us figure out which parts of the input data matter most for making predictions. Right now, there are some problems with current methods – they don’t do a great job of pinpointing the important features, and it takes a lot of computer power to use them on big datasets. The prospector heads method is different. It can work with any kind of data and any type of machine learning model, and it does a better job of finding the important features. This is important because it helps us understand how the models are making their predictions, which makes it easier for people to trust them. |
Keywords
* Artificial intelligence * Encoder * Machine learning
