How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks

12 September 2020

Papers citing "How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks"

8 / 8 papers shown

Title
One-shot skill assessment in high-stakes domains with limited data via meta learning Erim Yanik Steven D. Schwaitzberg Gene Yang Xavier Intes Jack Norfleet Matthew Hackett S. De 44 3 0 16 Dec 2022
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence Hayden Gunraj P. Guerrier Sheldon Fernandez Alexander Wong 9 8 0 18 Nov 2022
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces Aidan Boyd Patrick J. Tinsley Kevin W. Bowyer A. Czajka CVBM 24 9 0 22 Aug 2022
A deep learning model for burn depth classification using ultrasound imaging Sangrock Lee Rahul Rahul James Lukan Tatiana Boyko Kateryna Zelenova Basiel Makled Conner Parsey Jack Norfleet S. De MedIm 16 13 0 29 Mar 2022
Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning Erim Yanik Uwe Krüger Xavier Intes Rahul Rahul S. De 35 12 0 17 Mar 2022
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities Andrew Hryniowski Xiao Yu Wang A. Wong 22 10 0 30 Sep 2020
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,145 0 06 Jun 2015