4 code implementations • NeurIPS 2020 • Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
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.
no code implementations • 25 Sep 2019 • Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
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.
no code implementations • 13 May 2018 • Alexander Amini, Ava Soleimany, Sertac Karaman, Daniela Rus
Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.