11 citations · 18 across the 6 of their papers we have counts for
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cs.LG2023★ 1 cited
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
Antoine de Mathelin, François Deheeger, Mathilde Mougeot +1
This paper deals with uncertainty quantification and out-of-distribution detection in deep learning using Bayesian and ensemble methods. It proposes a practical solution to the lac…
cs.LG2023★ 4 cited
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot +1
We consider the problem of uncertainty quantification in high dimensional regression and classification for which deep ensemble have proven to be promising methods. Recent observat…