Summary of Confine: Conformal Prediction For Interpretable Neural Networks, by Linhui Huang et al.
CONFINE: Conformal Prediction for Interpretable Neural Networks
by Linhui Huang, Sayeri Lala, Niraj K. Jha
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 study introduces Conformal Prediction for Interpretable Neural Networks (CONFINE), a framework that generates prediction sets with uncertainty estimates to enhance model transparency and reliability. CONFINE provides example-based explanations, confidence estimates, and boosts accuracy by up to 3.6%. A new metric, correct efficiency, is defined to evaluate the fraction of prediction sets containing the correct label, achieving up to 3.3% higher than prior methods. CONFINE is adaptable to any pre-trained classifier and marks a significant advance towards transparent and trustworthy deep learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CONFINE is a new way for neural networks to work better in areas like healthcare where we need to understand how they make predictions. Right now, these networks are too secretive about their thinking, which makes it hard to trust them with important decisions. The current methods that try to explain what the networks are doing often sacrifice accuracy or don’t give us a good way to measure how sure we can be of their predictions. CONFINE changes this by giving us sets of possible answers instead of just one answer, along with a way to measure how likely each set is to contain the correct answer. |
Keywords
» Artificial intelligence » Deep learning
