On calibration of modern neural networks

A widely cited paper on the gap between a model’s confidence and its observed correctness.

This research paper examines calibration in modern neural networks. A calibrated system is one whose stated confidence corresponds, over a defined set, to how often it is correct. The paper helped make a practical problem visible: a model can perform well on classification while still expressing confidence that is too high or otherwise misleading.

Calibration does not turn a score into proof, and results from one dataset do not automatically transfer to live claims. The paper is useful because it separates being decisive from being reliable. In evidence-sensitive products, confidence should be tested against observed outcomes and shown alongside the task, data, and uncertainty that give it meaning.