论文标题
部分可观测时空混沌系统的无模型预测
A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement
论文作者
论文摘要
神经网络在许多任务中无处不在，但是信任他们的预测是一个开放的问题。许多应用需要不确定性量化，并且最好的核心和认知不确定性是最好的。在本文中，我们概括了产生分离不确定性的方法，以使用不同的不确定性量化方法，并评估其产生分离不确定性的能力。我们的研究结果表明：学习核心和认知不确定性之间存在相互作用，这是出乎意料的，并且违反了对不确定性的假设，诸如翻转的某些方法会产生零认知的不确定性，而出现的不确定性在分布外的环境中是不可靠的，并且合成提供了总体上的不挑剔的质量。我们还探讨了样品中的样品函数中的样本高参数数量产生的误差，建议n> 100个样本。我们预计我们的表述和结果可以帮助从业者和研究人员选择不确定性方法，并扩展使用分离不确定性的使用，并激励对该主题的其他研究。
Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods, and evaluate their capability to produce disentangled uncertainties. Our results show that: there is an interaction between learning aleatoric and epistemic uncertainty, which is unexpected and violates assumptions on aleatoric uncertainty, some methods like Flipout produce zero epistemic uncertainty, aleatoric uncertainty is unreliable in the out-of-distribution setting, and Ensembles provide overall the best disentangling quality. We also explore the error produced by the number of samples hyper-parameter in the sampling softmax function, recommending N > 100 samples. We expect that our formulation and results help practitioners and researchers choose uncertainty methods and expand the use of disentangled uncertainties, as well as motivate additional research into this topic.
