论文标题
使用元模型的深层顺序回归的不确定性预测
Uncertainty Prediction for Deep Sequential Regression Using Meta Models
论文作者
论文摘要
为顺序回归（尤其是深度复发网络）产生高质量的不确定性估计，仍然是一个具有挑战性和开放的问题。现有的方法通常会做出限制性假设（例如平稳性），但在实践中仍然表现较差，尤其是在现实世界中非平稳信号和漂移的情况下。本文介绍了一种灵活的方法，该方法可以产生对称和不对称的不确定性估计，对平稳性没有任何假设，并且在漂移和非漂移方案上都超越了竞争基线。这项工作有助于使顺序回归在现实世界应用中更有效，更实用，并且是用于顺序不确定性量化的建模工具箱的强大新补充。
Generating high quality uncertainty estimates for sequential regression, particularly deep recurrent networks, remains a challenging and open problem. Existing approaches often make restrictive assumptions (such as stationarity) yet still perform poorly in practice, particularly in presence of real world non-stationary signals and drift. This paper describes a flexible method that can generate symmetric and asymmetric uncertainty estimates, makes no assumptions about stationarity, and outperforms competitive baselines on both drift and non drift scenarios. This work helps make sequential regression more effective and practical for use in real-world applications, and is a powerful new addition to the modeling toolbox for sequential uncertainty quantification in general.
