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Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
24 May 2020
Sina Daubener
Lea Schonherr
Asja Fischer
D. Kolossa
AAML
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Papers citing
"Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification"
7 / 7 papers shown
Title
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution
Matías P. Pizarro
D. Kolossa
Asja Fischer
AAML
35
1
0
26 May 2023
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
Xinlei Zhou
Han Liu
Farhad Pourpanah
T. Zeng
Xizhao Wang
UQCV
UD
24
58
0
03 Nov 2021
An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions
Afshar Shamsi
Hamzeh Asgharnezhad
AmirReza Tajally
Saeid Nahavandi
Henry Leung
UQCV
44
6
0
07 Oct 2021
Dompteur: Taming Audio Adversarial Examples
Thorsten Eisenhofer
Lea Schonherr
Joel Frank
Lars Speckemeier
D. Kolossa
Thorsten Holz
AAML
33
24
0
10 Feb 2021
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems
Lea Schonherr
Thorsten Eisenhofer
Steffen Zeiler
Thorsten Holz
D. Kolossa
AAML
46
63
0
05 Aug 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,661
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
1