Principled Uncertainty Estimation for Deep Neural Networks

By Richard E. Harang, Ethan M. Rudd. 2018

3 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

1. Shreyas Padhy
2. Balaji Lakshminarayanan
3. Zi Lin
4. Tania Bedrax-Weiss
5. Jeremiah Zhe Liu
6. Dustin Tran

In Probabilistic & Bayesian Methods

1. Balaji Lakshminarayanan
2. Zi Lin
3. Shreyas Padhy
4. Tania Bedrax-Weiss
5. Jeremiah Zhe Liu
6. Dustin Tran
7. David M. Blei
8. Rajesh Ranganath

Citations in the corpus

• Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, background): “…an intuitive form, and has already been used widely as a training objective in many uncertainty works that leverage adversarial training and generative modeling to detect OOD examples (Hafner et al., 2018; Harang and Rudd, 2018; Lee et al., 2018; Malinin and Gales, 2018b; Meinke and Hein, 2020).”