Evidential Deep Learning models uncertainty with Dirichlet distributions, but its foundations are shaky. This update uses density-informed pseudo-counts to improve calibration. It's a step towards more reliable uncertainty-aware classification, but we don't know yet if it holds up outside benchmarks.
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