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.
翻译:为连续回归,特别是深层的经常性网络产生高质量的不确定性估计数,仍然是一个挑战性和未解决的问题;现有办法往往使限制性假设(如静态)在实际中表现不佳,特别是在现实世界的非静止信号和漂移的情况下。本文描述了一种灵活的方法,这种方法可以产生对称和不对称的不确定性估计数,不对稳定性作任何假设,在漂移和非漂移情景上都优于竞争性基线。这项工作有助于使连续回归更加有效和实用,供现实世界应用使用,并且是对一般连续不确定性量化模型工具箱的有力新补充。