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Prospector Heads: Generalized Feature Attribution for Large Models & Data
Gautam Machiraju1, Alexander Derry1, Arjun Desai2
1Department of Biomedical Data Science, Stanford University.
Arxiv
|July 1, 2024
Summary
Prospector heads offer efficient and interpretable feature attribution for machine learning models, outperforming existing methods in localization accuracy across various data types. This advancement enhances trust and transparency in complex scientific and biomedical applications.
Area of Science:
- Machine Learning
- Scientific Computing
- Biomedical Informatics
Background:
- Feature attribution is crucial for ML interpretability in science and medicine.
- Existing methods struggle with precise localization, small sample sizes, and high-dimensional data.
Purpose of the Study:
- Introduce prospector heads as an efficient and interpretable alternative to explanation-based attribution.
- Demonstrate the generalizability and performance of prospector heads across diverse data modalities.
Main Methods:
- Developed prospector heads, a novel approach applicable to any encoder and data type.
- Validated prospector heads on sequence (text), image (pathology), and graph (protein structure) data.
Main Results:
- Prospector heads achieved superior localization accuracy, outperforming baselines by up to 26.3 points in mean localization AUPRC.
- Demonstrated improved interpretation and discovery of class-specific patterns.
Conclusions:
- Prospector heads offer a high-performance, flexible, and generalizable framework for feature attribution.
- This method enhances trust and transparency for ML models in scientific and biomedical domains.
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