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Found 3 papers, 1 papers with code
Deep Evidential Regression
We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
Deep Evidential Uncertainty
In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.
Spatial Uncertainty Sampling for End-to-End Control
Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.
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