English(EN) Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks

新方法联合优化神经网络精度和校准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一种新颖的方法，可以联合优化神经网络的精度和校准，无需单独进行事后校准步骤。这种双层Stackelberg公式将校准直接集成到训练过程中，使用交叉熵同时改进这两个指标。该方法表明，使用最终分类层作为校准器足以以最小的附加复杂性实现精确且校准良好的模型，其性能优于现有的最先进的校准技术。 AI

影响这项研究可能导致各种AI应用中更可靠、更值得信赖的神经网络输出。

排序理由该集群包含一篇详细介绍神经网络校准新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Nektarios Kalampalikis, Pablo M. Olmos, Isabel Valera · 2026-10-12 04:00

一站式校准：联合学习精确且校准的神经网络

arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already trained model using a separate calibration dataset and additional calibration p…

本文如何被排名

报道来源 [1]

一站式校准：联合学习精确且校准的神经网络

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