URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

8 July 2020

Papers citing "URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks"

4 / 4 papers shown

Title
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications J. Rock Tiago Azevedo R. D. Jong Daniel Ruiz-Munoz Partha P. Maji UQCV 13 5 0 11 Nov 2021
Post-hoc loss-calibration for Bayesian neural networks Meet P. Vadera S. Ghosh Kenney Ng Benjamin M. Marlin UQCV BDL 28 7 0 13 Jun 2021
DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou Moksh Jain Hadi Nekoei V. Butoi Paul Bertin Jarrid Rector-Brooks Maksym Korablyov Yoshua Bengio PER UQLM UQCV UD 200 81 0 16 Feb 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 279 9,136 0 06 Jun 2015