On the Robustness of Monte Carlo Dropout Trained with Noisy Labels

22 March 2021

Papers citing "On the Robustness of Monte Carlo Dropout Trained with Noisy Labels"

6 / 6 papers shown

Title
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks Aidan Furlong Farah Alsafadi S. Palmtag Andrew Godfrey Xu Wu 30 1 0 27 Jun 2024
Sparse Double Descent: Where Network Pruning Aggravates Overfitting Zhengqi He Zeke Xie Quanzhi Zhu Zengchang Qin 56 27 0 17 Jun 2022
Maximum Likelihood Uncertainty Estimation: Robustness to Outliers Deebul Nair Nico Hochgeschwender Miguel A. Olivares-Mendez OOD 11 7 0 03 Feb 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 136 679 0 31 Jan 2021
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 47 170 0 24 May 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015