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May, 2024
关于测量离散概率神经网络的校准
On Measuring Calibration of Discrete Probabilistic Neural Networks
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Spencer Young, Porter Jenkins
TL;DR
使用条件核平均嵌入测量标定差异，以提高机器学习模型的不确定性量化，并消除偏差和参数假设。初步实验基于合成数据展示了该方法的潜力，并计划用于更复杂的应用。
Abstract
As
machine learning
systems become increasingly integrated into real-world applications, accurately representing uncertainty is crucial for enhancing their safety, robustness, and reliability. Training
neural networks
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