Summary of Improving Predictor Reliability with Selective Recalibration, by Thomas P. Zollo et al.
Improving Predictor Reliability with Selective Recalibration
by Thomas P. Zollo, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 In this paper, researchers focus on developing reliable deep learning systems by improving the accuracy of confidence estimates in model predictions. They propose “selective recalibration,” a novel approach that learns to reject specific data points to allow for better recalibration. This method leverages feature embedding spaces rather than output spaces and uses a selection model to identify areas where recalibration is most effective. Theoretical analysis supports this algorithm, and experiments on medical imaging and zero-shot classification tasks demonstrate its superiority over various baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making deep learning systems more trustworthy by getting better at predicting how sure they are of their answers. Right now, some methods can do a good job at this, but they only work well with small amounts of data. The researchers in this study come up with a new approach called “selective recalibration.” Instead of trying to fix the whole system, it finds areas where the system is most likely to be wrong and ignores those parts. They show that their method works better than others on tricky tasks like medical image analysis. |
Keywords
» Artificial intelligence » Classification » Deep learning » Zero shot
