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Regression Prior Networks

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arxiv 2006.11590 v2 pith:2CXNNIUG submitted 2020-06-20 cs.LG stat.ML

Andrey Malinin , Sergey Chervontsev , Ivan Provilkov , Mark Gales This is my paper · ORCID
classification cs.LGstat.ML
keywords networkspriorensembleregressiontasksapproachesbeendeveloped
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abstract

Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on a range of tasks. They can also be used to distill an ensemble of models via Ensemble Distribution Distillation (EnD$^2$), such that its accuracy, calibration and uncertainty estimates are retained within a single model. However, Prior Networks have so far been developed only for classification tasks. This work extends Prior Networks and EnD$^2$ to regression tasks by considering the Normal-Wishart distribution. The properties of Regression Prior Networks are demonstrated on synthetic data, selected UCI datasets and a monocular depth estimation task, where they yield performance competitive with ensemble approaches.

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Cited by 3 Pith papers

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