Plug-in losses approximate EDL training objectives at the Dirichlet mean with decaying error as evidence grows, including softmax under a specific mapping, and match classical EDL performance on Google Speech Commands.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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UNVERDICTED 2representative citing papers
DAPPr introduces a possibilistic framework that projects parameter posteriors to predictions via supremum and approximates them with Dirichlet possibility functions to yield efficient, closed-form epistemic uncertainty estimates.
citing papers explorer
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Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier
Plug-in losses approximate EDL training objectives at the Dirichlet mean with decaying error as evidence grows, including softmax under a specific mapping, and match classical EDL performance on Google Speech Commands.
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Possibilistic Predictive Uncertainty for Deep Learning
DAPPr introduces a possibilistic framework that projects parameter posteriors to predictions via supremum and approximates them with Dirichlet possibility functions to yield efficient, closed-form epistemic uncertainty estimates.