Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data

5 April 2021

Papers citing "Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data"

6 / 6 papers shown

Title
A Cough-based deep learning framework for detecting COVID-19 Hoang Van Truong L. D. Pham Dat Ngo Hoang-Dung Nguyen 14 7 0 07 Oct 2021
Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge N. Sharma Ananya Muguli Prashant Krishnan Rohit Kumar Srikanth Raj Chetupalli Sriram Ganapathy 12 13 0 21 Jun 2021
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates Björn W. Schuller A. Batliner Christian Bergler Cecilia Mascolo Jing Han ... Pietro Cicuta L. Rothkrantz J. Zwerts Jelle Treep Casper S. Kaandorp 47 111 0 24 Feb 2021
Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks Björn W. Schuller H. Coppock Alexander Gaskell 22 63 0 29 Dec 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015