Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring

3 July 2020

Papers citing "Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring"

4 / 4 papers shown

Title
An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications Hans-Martin Heyn E. Knauss Iswarya Malleswaran Shruthi Dinakaran 32 4 0 31 Jan 2023
MLDemon: Deployment Monitoring for Machine Learning Systems Antonio A. Ginart Martin Jinye Zhang James Zou 54 18 0 28 Apr 2021
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends Q. Rahman Peter Corke Feras Dayoub OOD 44 51 0 05 Jan 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015