Success of Uncertainty-Aware Deep Models Depends on Data Manifold
Geometry

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry

2 August 2022

Harrison Termotto

Finale Doshi-Velez

Papers citing "Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry"

5 / 5 papers shown

Title
Model uncertainty quantification using feature confidence sets for outcome excursions Junting Ren Armin Schwartzman 51 0 0 28 Apr 2025
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 189 259 0 18 Apr 2021
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 60 171 0 08 Jul 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015