Related papers: Uncertainty Prediction for Deep Sequential Regression Using Meta Models

Uncertainty Prediction for Deep Sequential Regression Using Meta Models

URL: http://arxiv.org/abs/2007.01350v2
Date: Thu, 22 Jul 2021 21:59:50 GMT
Title: Uncertainty Prediction for Deep Sequential Regression Using Meta Models
Authors: Jiri Navratil, Matthew Arnold, Benjamin Elder
Abstract summary: This paper describes a flexible method that can generate symmetric and asymmetric uncertainty estimates. It makes no assumptions about stationarity, and outperforms competitive baselines on both drift and non drift scenarios.
Score: 4.189643331553922
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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