GEM-FI: Gated Evidential Mixtures with Fisher Modulation
Evidential Deep Learning (EDL) enables single-pass uncertainty estimation by predicting Dirichlet evidence, but it can remain overconfident and poorly calibrated, and it often fails to represent multi-modal epistemic uncertainty. We introduce Gated Evidential Mixtures (GEM), a family of models that learns an in-model energy signal and uses it to gate evidential outputs end-to-end in a distance-informed manner. GEM-CORE learns a feature-level energy and maps it to a bounded gate that smoothly suppresses evidence when support is low. To capture epistemic multi-modality without multi-pass ensembling, GEM-MIX adds a lightweight mixture of evidential heads with learned routing weights while pr...
| Authors | Marco Mustafa Mohammed, Fatemeh Daneshfar, Pietro Liò |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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