Uncertainty Prediction for Deep Sequential Regression Using Meta Models

Jiri Navratil, Matthew Arnold, Benjamin Elder

Keywords: Uncertainty Quantification, Uncertainty Prediction, Deep Learning, Regression, Meta Modeling

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.

One-sentence Summary: Meta modeling is highly effective in uncertainty quantification in sequential regression tasks using deep recurrent NNs.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2007.01350/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=RjGfKb5Z3j