# Calibration of Neural Networks

arXiv: https://arxiv.org/abs/2303.10761 · HTML: https://mlsanity.com/paper/2303.10761/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [Calibrating Deep Neural Networks using Focal Loss](https://mlsanity.com/paper/2002.09437/) (2020)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)

## Built on by (most cited first, 7 of 7)

- [Algorithms with Calibrated Machine Learning Predictions](https://mlsanity.com/paper/2502.02861/) (2025)
- [On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning](https://mlsanity.com/paper/2311.12688/) (2023)
- [Survival Analysis with Adversarial Regularization](https://mlsanity.com/paper/2312.16019/) (2023)
- [Forecasting VIX using Bayesian Deep Learning](https://mlsanity.com/paper/2401.17042/) (2024)
- [On the calibration of Just-in-time Defect Prediction](https://mlsanity.com/paper/2504.12051/) (2025)
- [Deep Linear Discriminant Analysis Revisited](https://mlsanity.com/paper/2601.01619/) (2026)
- [Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling](https://mlsanity.com/paper/2604.13271/) (2026)

## Abstract (arXiv)

Neural networks solving real-world problems are often required not only to make accurate predictions but also to provide a confidence level in the forecast. The calibration of a model indicates how close the estimated confidence is to the true probability. This paper presents a survey of confidence calibration problems in the context of neural networks and provides an empirical comparison of calibration methods. We analyze problem statement, calibration definitions, and different approaches to evaluation: visualizations and scalar measures that estimate whether the model is well-calibrated. We review modern calibration techniques: based on post-processing or requiring changes in training. Empirical experiments cover various datasets and models, comparing calibration methods according to different criteria.