论文标题
为什么有统一的预测不确定性？使用深层的合奏将其解开
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles
论文作者
论文摘要
当在医疗保健等现实世界中部署时，黑匣子神经网络（NNS）中的理解和量化不确定性至关重要。使用贝叶斯和非乘坐方法的最新作品显示了如何为NNS建模统一的预测不确定性。将这种不确定性分解为消除数据中异质性的颗粒来源提供了有关其基本原因的丰富信息。我们提出了一种概念上简单的非乘坐方法，即深拆分集合，以使用多元高斯混合模型来解散预测性不确定性。 NNS经过培训的输入特征簇，以获取每个群集的不确定性估计。我们在一系列基准回归数据集上评估了我们的方法，同时还与统一的不确定性方法进行了比较。使用数据集刺耳的广泛分析和经验规则突出了我们固有的良好校准模型。我们的工作进一步证明了其在多模式设置中使用基准测试的阿尔茨海默氏症数据集的适用性，还展示了深层分裂集合可以突出显示隐藏的模态特定偏见的程度。 NNS所需的最小变化和培训程序以及将小组功能伸入簇中的高灵活性使其很容易部署和有用。源代码可从https://github.com/wazeerzulfikar/deep-split-semembles获得
Understanding and quantifying uncertainty in black box Neural Networks (NNs) is critical when deployed in real-world settings such as healthcare. Recent works using Bayesian and non-Bayesian methods have shown how a unified predictive uncertainty can be modelled for NNs. Decomposing this uncertainty to disentangle the granular sources of heteroscedasticity in data provides rich information about its underlying causes. We propose a conceptually simple non-Bayesian approach, deep split ensemble, to disentangle the predictive uncertainties using a multivariate Gaussian mixture model. The NNs are trained with clusters of input features, for uncertainty estimates per cluster. We evaluate our approach on a series of benchmark regression datasets, while also comparing with unified uncertainty methods. Extensive analyses using dataset shits and empirical rule highlight our inherently well-calibrated models. Our work further demonstrates its applicability in a multi-modal setting using a benchmark Alzheimer's dataset and also shows how deep split ensembles can highlight hidden modality-specific biases. The minimal changes required to NNs and the training procedure, and the high flexibility to group features into clusters makes it readily deployable and useful. The source code is available at https://github.com/wazeerzulfikar/deep-split-ensembles
