Improving the repeatability of deep learning models with Monte Carlo
dropout

Improving the repeatability of deep learning models with Monte Carlo dropout

15 February 2022

Christopher P. Bridge

Silvia De Sanjosé

John Peter Campbell

Jayashree Kalpathy-Cramer

Papers citing "Improving the repeatability of deep learning models with Monte Carlo dropout"

5 / 5 papers shown

Title
Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis Nikita Ravi Abhinav Goel James C. Davis George K. Thiruvathukal 51 0 0 06 May 2025
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 57 1 0 27 Jun 2024
SkiNet: A Deep Learning Solution for Skin Lesion Diagnosis with Uncertainty Estimation and Explainability R. Singh R. Gorantla Sai Giridhar Allada N. Pratap 19 3 0 30 Dec 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,638 0 03 Jul 2012