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. 2307.03217
  4. Cited By
Quantification of Uncertainty with Adversarial Models

Quantification of Uncertainty with Adversarial Models

6 July 2023
Kajetan Schweighofer
L. Aichberger
Mykyta Ielanskyi
G. Klambauer
Sepp Hochreiter
    UQCV
ArXivPDFHTML

Papers citing "Quantification of Uncertainty with Adversarial Models"

18 / 18 papers shown
Title
Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model
Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model
Xiannan Huang
Shuhan Qiu
AI4TS
14
0
0
13 May 2025
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
L. J. L. Lopez
Shaza Elsharief
Dhiyaa Al Jorf
Firas Darwish
Congbo Ma
Farah E. Shamout
52
0
0
04 May 2025
Adversarial Examples in Environment Perception for Automated Driving (Review)
Adversarial Examples in Environment Perception for Automated Driving (Review)
Jun Yan
Huilin Yin
AAML
34
0
0
11 Apr 2025
The Disparate Benefits of Deep Ensembles
The Disparate Benefits of Deep Ensembles
Kajetan Schweighofer
Adrián Arnaiz-Rodríguez
Sepp Hochreiter
Nuria Oliver
FedML
18
1
0
17 Oct 2024
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization
Johan Hatleskog
Kostas Alexis
3DPC
30
0
0
14 Oct 2024
PH-Dropout: Practical Epistemic Uncertainty Quantification for View
  Synthesis
PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis
Chuanhao Sun
Thanos Triantafyllou
Anthos Makris
Maja Drmač
Kai Xu
Luo Mai
Mahesh Marina
28
0
0
07 Oct 2024
Achieving Well-Informed Decision-Making in Drug Discovery: A
  Comprehensive Calibration Study using Neural Network-Based Structure-Activity
  Models
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models
Hannah Rosa Friesacher
O. Engkvist
Lewis H. Mervin
Yves Moreau
Adam Arany
24
0
0
19 Jul 2024
Semantically Diverse Language Generation for Uncertainty Estimation in
  Language Models
Semantically Diverse Language Generation for Uncertainty Estimation in Language Models
L. Aichberger
Kajetan Schweighofer
Mykyta Ielanskyi
Sepp Hochreiter
HILM
23
10
0
06 Jun 2024
Epistemic Uncertainty Quantification For Pre-trained Neural Network
Epistemic Uncertainty Quantification For Pre-trained Neural Network
Hanjing Wang
Qiang Ji
UQCV
23
2
0
15 Apr 2024
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A
  Survey
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Naveen Karunanayake
Ravin Gunawardena
Suranga Seneviratne
Sanjay Chawla
OOD
38
5
0
08 Apr 2024
Introducing an Improved Information-Theoretic Measure of Predictive
  Uncertainty
Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty
Kajetan Schweighofer
L. Aichberger
Mykyta Ielanskyi
Sepp Hochreiter
10
11
0
14 Nov 2023
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from
  Spatial Aleatoric Uncertainty
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty
Kilian Zepf
Selma Wanna
M. Miani
Juston Moore
J. Frellsen
Søren Hauberg
Aasa Feragen
Frederik Warburg
UQCV
16
4
0
23 Mar 2023
Variational Neural Networks
Variational Neural Networks
Illia Oleksiienko
D. Tran
Alexandros Iosifidis
BDL
UQCV
21
8
0
04 Jul 2022
Multiple Importance Sampling ELBO and Deep Ensembles of Variational
  Approximations
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
Oskar Kviman
Harald Melin
Hazal Koptagel
Victor Elvira
J. Lagergren
DRL
63
17
0
22 Feb 2022
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space
  Perspective
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Luca Scimeca
Seong Joon Oh
Sanghyuk Chun
Michael Poli
Sangdoo Yun
OOD
374
49
0
06 Oct 2021
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in
  Gaussian Process Hybrid Deep Networks
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
60
171
0
08 Jul 2017
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
268
5,652
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
247
9,109
0
06 Jun 2015
1