Summary of Calibrating Where It Matters: Constrained Temperature Scaling, by Stephen Mckenna and Jacob Carse
Calibrating Where It Matters: Constrained Temperature Scaling
by Stephen McKenna, Jacob Carse
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a new approach to calibrating convolutional neural networks (convnets) for diagnostic decision making in medical imaging. The goal is to minimize expected costs by tuning the classifier’s calibration in regions of probability simplex that are most likely to affect decisions. This is achieved by modifying temperature scaling calibration and training convnets to classify dermoscopy images. The approach shows improved calibration where it matters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to help doctors make better diagnoses by calibrating their computer models, which can be used for medical imaging analysis. It’s like trying to guess the right answer from a list of possibilities. The researchers came up with a new way to do this using special algorithms and training data, which shows they are able to improve the accuracy when it counts. |
Keywords
* Artificial intelligence * Probability * Temperature
