Summary of Accuracy-preserving Calibration Via Statistical Modeling on Probability Simplex, by Yasushi Esaki and Akihiro Nakamura and Keisuke Kawano and Ryoko Tokuhisa and Takuro Kutsuna
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
by Yasushi Esaki, Akihiro Nakamura, Keisuke Kawano, Ryoko Tokuhisa, Takuro Kutsuna
First submitted to arxiv on: 21 Feb 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 Classification models based on deep neural networks (DNNs) require calibration to measure prediction reliability. Recent probabilistic model-based methods have been proposed, but they cannot preserve the accuracy of pre-trained models, even those with high classification accuracy. Our proposed method uses the Concrete distribution as a probabilistic model on the probability simplex and theoretically proves that DNNs trained on cross-entropy loss have optimality as the Concrete distribution parameter. We also present an efficient method for generating synthetic samples for training probabilistic models on the probability simplex. Experimental results demonstrate that our method outperforms previous methods in accuracy-preserving calibration tasks using benchmark datasets. The proposed approach is particularly useful for applications where accurate predictions are crucial, such as self-driving cars or medical diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure deep learning models make accurate predictions. Currently, some methods try to fix the problem of inaccurate predictions by using a special type of model called a probabilistic model on a thing called the probability simplex. However, these methods don’t work well with pre-trained models that are already very good at making predictions. The researchers in this paper came up with a new way to do calibration using something called the Concrete distribution. They showed that their method works really well and can be used to improve predictions for things like self-driving cars or medical diagnosis. |
Keywords
* Artificial intelligence * Classification * Cross entropy * Deep learning * Probabilistic model * Probability
