论文标题
UQ武装：对对抗规范化的混合效应深度学习的不确定性量化，用于聚类的非IID数据
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data
论文作者
论文摘要
这项工作证明了为模型拟合，固定效应协方差系数和预测信心生成易于解释的统计指标的能力。重要的是，这项工作比较了4种合适且常用的认知UQ方法，即BNN，SWAG，MC Dropout和Ensemble方法，以计算武装MEDL模型的这些统计指标的能力。在我们的AD预后实验中，UQ方法不仅提供了这些好处，而且几种UQ方法保持了原始武装方法的高性能，有些方法甚至提供了适度（但没有统计学意义）的性能改善。合奏模型，尤其是具有90％子采样的集合方法，在我们测试的所有指标中都表现良好，（1）高性能与非UQ武装模型相当的高性能，（2）适当地将混杂探针降低并分配了统计上的PVALUES P-ValueS，（3）实验p-Values，（3）（3）（3）相对较高的高度高校准的输出预测置信度相对较高。根据结果，合奏的方法，尤其是90％的亚采样，为预测和不确定性估计提供了最佳的全面性能，并实现了我们的目标，以提供模型拟合的统计意义，统计显着性协值协方差系数，以及对预测的信心，同时使用MEDL使用Armed Medl的基线表现来提供统计学意义
This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence. Importantly, this work compares 4 suitable and commonly applied epistemic UQ approaches, BNN, SWAG, MC dropout, and ensemble approaches in their ability to calculate these statistical metrics for the ARMED MEDL models. In our experiment for AD prognosis, not only do the UQ methods provide these benefits, but several UQ methods maintain the high performance of the original ARMED method, some even provide a modest (but not statistically significant) performance improvement. The ensemble models, especially the ensemble method with a 90% subsampling, performed well across all metrics we tested with (1) high performance that was comparable to the non-UQ ARMED model, (2) properly deweights the confounds probes and assigns them statistically insignificant p-values, (3) attains relatively high calibration of the output prediction confidence. Based on the results, the ensemble approaches, especially with a subsampling of 90%, provided the best all-round performance for prediction and uncertainty estimation, and achieved our goals to provide statistical significance for model fit, statistical significance covariate coefficients, and confidence in prediction, while maintaining the baseline performance of MEDL using ARMED
