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.The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement
num. 101069595 and the European Research Council (ERC)
under the European Union’s Horizon 2020 research and
innovation programme (grant agreement No. 772773).
Additionally, this work has been partially supported by
Grant PID2019-107255GB-C21 funded by MCIN/AEI/
10.13039/501100011033.Peer ReviewedPostprint (published version
