ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.14611
  4. Cited By
Detecting Adversarial Examples for Speech Recognition via Uncertainty
  Quantification

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification

24 May 2020
Sina Daubener
Lea Schonherr
Asja Fischer
D. Kolossa
    AAML
ArXivPDFHTML

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
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
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
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
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
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
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
1