Retrospective Uncertainties for Deep Models using Vine Copulas

Nataša Tagasovska; Firat Ozdemir; Axel Brando

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arXiv · 2302.12606

Retrospective Uncertainties for Deep Models using Vine Copulas

Nataša Tagasovska·Firat Ozdemir·Axel Brando

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Abstract

Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectural changes. We propose to compensate for the lack of built-in uncertainty estimates by supplementing any network, retrospectively, with a subsequent vine copula model, in an overall compound we call Vine-Copula Neural Network (VCNN). Through synthetic and real-data experiments, we show that VCNNs could be task (regression/classification) and architecture (recurrent, fully connected) agnostic while providing reliable and better-calibrated uncertainty estimates, comparable to state-of-the-art built-in uncertainty solutions.

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Nataša Tagasovska, Firat Ozdemir, Axel Brando. 2023-02-24. Retrospective Uncertainties for Deep Models using Vine Copulas. https://arxiv.org/abs/2302.12606

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