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2012.10923
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Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration
AAAI Conference on Artificial Intelligence (AAAI), 2019
20 December 2020
Christian Tomani
Florian Buettner
UQCV
AAML
OOD
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Papers citing
"Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration"
29 / 29 papers shown
Towards Calibration Enhanced Network by Inverse Adversarial Attack
Yupeng Cheng
Zi Pong Lim
Sarthak Ketanbhai Modi
Yon Shin Teo
Yushi Cao
Shang-Wei Lin
AAML
180
0
0
08 Apr 2025
CER: Confidence Enhanced Reasoning in LLMs
Annual Meeting of the Association for Computational Linguistics (ACL), 2025
Ali Razghandi
Seyed Mohammad Hadi Hosseini
Mahdieh Soleymani Baghshah
LRM
651
11
0
20 Feb 2025
Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
Knowledge Discovery and Data Mining (KDD), 2024
Thomas Decker
Alexander Koebler
Michael Lebacher
Ingo Thon
Volker Tresp
Florian Buettner
297
2
0
24 Aug 2024
How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning
Giuseppe Serra
Ben Werner
Florian Buettner
480
6
0
10 Jul 2024
Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond
Giuseppe Serra
Florian Buettner
CLL
FedML
462
0
0
29 May 2024
Towards Certification of Uncertainty Calibration under Adversarial Attacks
Cornelius Emde
Francesco Pinto
Thomas Lukasiewicz
Juil Sock
Adel Bibi
AAML
603
2
0
22 May 2024
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
Wonjeong Choi
Jun-Gyu Park
Dong-Jun Han
Younghyun Park
Jaekyun Moon
384
2
0
22 Feb 2024
Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models
Pei Wang
Yejie Wang
Muxi Diao
Keqing He
Guanting Dong
Weiran Xu
367
2
0
17 Feb 2024
Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence
Stephen Obadinma
Xiaodan Zhu
Ziqiao Wang
AAML
313
3
0
05 Jan 2024
U-Trustworthy Models.Reliability, Competence, and Confidence in Decision-Making
AAAI Conference on Artificial Intelligence (AAAI), 2024
Ritwik Vashistha
Arya Farahi
326
2
0
04 Jan 2024
SimCalib: Graph Neural Network Calibration based on Similarity between Nodes
Boshi Tang
Zhiyong Wu
Xixin Wu
Qiaochu Huang
Jun Chen
Shunwei Lei
Helen M. Meng
247
13
0
19 Dec 2023
Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning
Shiyang Yan
Zongxuan Liu
Lin Xu
311
2
0
12 Oct 2023
Understanding Calibration of Deep Neural Networks for Medical Image Classification
A. Sambyal
Usma Niyaz
N. C. Krishnan
Deepti R. Bathula
379
26
0
22 Sep 2023
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
International Conference on Learning Representations (ICLR), 2023
Miao Xiong
Zhiyuan Hu
Xinyang Lu
Yifei Li
Jie Fu
Junxian He
Bryan Hooi
620
811
0
22 Jun 2023
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
International Conference on Machine Learning (ICML), 2023
Deepshikha Pandey
Qi Yu
EDL
297
29
0
19 Jun 2023
Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping
Neural Information Processing Systems (NeurIPS), 2023
Jia-Qi Yang
De-Chuan Zhan
Le Gan
UQCV
341
7
0
08 Jun 2023
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
International Conference on Machine Learning (ICML), 2023
Christian Tomani
Futa Waseda
Yuesong Shen
Zorah Lähner
UQCV
313
16
0
10 Feb 2023
Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks
Erdong Guo
D. Draper
Maria de Iorio
294
1
0
27 Dec 2022
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Sebastian G. Gruber
Florian Buettner
PER
UQCV
UD
639
21
0
21 Oct 2022
Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing
Patterns (Patterns), 2022
Georgios Rizos
J. Lawson
Simon Mitchell
Pranay Shah
Xin Wen
Cristina Banks‐Leite
R. Ewers
Bjoern W. Schuller
UQCV
286
3
0
19 Oct 2022
What Makes Graph Neural Networks Miscalibrated?
Neural Information Processing Systems (NeurIPS), 2022
Hans Hao-Hsun Hsu
Yuesong Shen
Christian Tomani
Zorah Lähner
324
49
0
12 Oct 2022
Robust Models are less Over-Confident
Neural Information Processing Systems (NeurIPS), 2022
Julia Grabinski
Paul Gavrikov
J. Keuper
Margret Keuper
AAML
345
33
0
12 Oct 2022
Improved post-hoc probability calibration for out-of-domain MRI segmentation
Cheng Ouyang
Shuo Wang
Chong Chen
Zeju Li
Wenjia Bai
Bernhard Kainz
Daniel Rueckert
UQCV
MedIm
220
5
0
04 Aug 2022
On Calibration of Graph Neural Networks for Node Classification
IEEE International Joint Conference on Neural Network (IJCNN), 2022
Tong Liu
Yushan Liu
Marcel Hildebrandt
Mitchell Joblin
Hang Li
Volker Tresp
359
11
0
03 Jun 2022
CHALLENGER: Training with Attribution Maps
Christian Tomani
Zorah Lähner
171
1
0
30 May 2022
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
K. Wagstaff
Thomas G. Dietterich
314
1
0
03 Feb 2022
Training on Test Data with Bayesian Adaptation for Covariate Shift
Aurick Zhou
Sergey Levine
OOD
TTA
332
13
0
27 Sep 2021
When and How Mixup Improves Calibration
International Conference on Machine Learning (ICML), 2021
Linjun Zhang
Zhun Deng
Kenji Kawaguchi
James Zou
UQCV
437
79
0
11 Feb 2021
Post-hoc Uncertainty Calibration for Domain Drift Scenarios
Computer Vision and Pattern Recognition (CVPR), 2020
Christian Tomani
Sebastian Gruber
Muhammed Ebrar Erdem
Zorah Lähner
Florian Buettner
UQCV
409
86
0
20 Dec 2020
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