11 citations · 26 across the 21 of their papers we have counts for
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cs.LG2024★ 1 cited
LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies
Jia Shi, Gautam Gare, Jinjin Tian +6
We tackle the challenge of predicting models' Out-of-Distribution (OOD) performance using in-distribution (ID) measurements without requiring OOD data. Existing evaluations with "E…
cs.LG2019★ 2 cited
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
Janis Postels, Francesco Ferroni, Huseyin Coskun +2
We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural ne…