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Fellaji et al., 2024 - Google Patents

On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss

Fellaji et al., 2024

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Document ID
6177550910705347315
Author
Fellaji M
Pennerath F
Conan-Guez B
Couceiro M
Publication year
Publication venue
Joint European Conference on Machine Learning and Knowledge Discovery in Databases

External Links

Snippet

The calibration of predictive distributions has been widely studied in deep learning, but the same cannot be said about the more specific epistemic uncertainty as produced by Deep Ensembles, Bayesian Deep Networks, or Evidential Deep Networks. Although measurable …
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    • G—PHYSICS
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