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.
@article{arxiv.2302.12606,
title = {Retrospective Uncertainties for Deep Models using Vine Copulas},
author = {Nataša Tagasovska and Firat Ozdemir and Axel Brando},
journal= {arXiv preprint arXiv:2302.12606},
year = {2023}
}