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2012.13052
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Learning from Crowds by Modeling Common Confusions
AAAI Conference on Artificial Intelligence (AAAI), 2020
24 December 2020
Zhendong Chu
Jing Ma
Hongning Wang
NoLa
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Papers citing
"Learning from Crowds by Modeling Common Confusions"
28 / 28 papers shown
Active Query Selection for Crowd-Based Reinforcement Learning
Jonathan Erskine
Taku Yamagata
Raúl Santos-Rodríguez
202
0
0
26 Aug 2025
Understanding the Essence: Delving into Annotator Prototype Learning for Multi-Class Annotation Aggregation
Ju Chen
Jun Feng
Shenyu Zhang
110
0
0
04 Aug 2025
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels
M. Herde
Lukas Lührs
Denis Huseljic
Bernhard Sick
450
0
0
12 Apr 2025
Learning from Noisy Labels via Conditional Distributionally Robust Optimization
Neural Information Processing Systems (NeurIPS), 2024
Hui Guo
Grace Y. Yi
Boyu Wang
NoLa
402
6
0
26 Nov 2024
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
M. Herde
Denis Huseljic
Lukas Rauch
Bernhard Sick
411
5
0
30 Jul 2024
Mixture of Experts based Multi-task Supervise Learning from Crowds
Tao Han
Huaixuan Shi
Xinyi Ding
Xiao Ma
Huamao Gu
Yili Fang
252
6
0
18 Jul 2024
Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?
Tanguy Lefort
Antoine Affouard
Benjamin Charlier
J. Lombardo
Mathias Chouet
Hervé Goëau
Joseph Salmon
P. Bonnet
Alexis Joly
344
6
0
05 Jun 2024
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
European Conference on Artificial Intelligence (ECAI), 2024
M. Herde
Lukas Lührs
Denis Huseljic
Bernhard Sick
396
4
0
06 May 2024
Coupled Confusion Correction: Learning from Crowds with Sparse Annotations
AAAI Conference on Artificial Intelligence (AAAI), 2023
Hansong Zhang
Shikun Li
Dan Zeng
Chenggang Yan
Shiming Ge
387
23
0
12 Dec 2023
Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Philipp Heinisch
Matthias Orlikowski
Julia Romberg
Philipp Cimiano
328
12
0
06 Nov 2023
Label Selection Approach to Learning from Crowds
International Conference on Neural Information Processing (ICONIP), 2023
Kosuke Yoshimura
H. Kashima
NoLa
144
1
0
21 Aug 2023
Evaluating AI systems under uncertain ground truth: a case study in dermatology
David Stutz
A. Cemgil
Abhijit Guha Roy
Tatiana Matejovicova
Melih Barsbey
...
Yossi Matias
Pushmeet Kohli
Yao Xiao
Arnaud Doucet
Alan Karthikesalingam
357
3
0
05 Jul 2023
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization
International Conference on Learning Representations (ICLR), 2023
Shahana Ibrahim
Tri Nguyen
Xiao Fu
252
26
0
05 Jun 2023
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Shikun Li
Xiaobo Xia
Jiankang Deng
Shiming Ge
Tongliang Liu
351
22
0
05 Jun 2023
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
International Conference on Machine Learning (ICML), 2023
Tri Nguyen
Shahana Ibrahim
Xiao Fu
209
8
0
30 May 2023
Multi-annotator Deep Learning: A Probabilistic Framework for Classification
M. Herde
Denis Huseljic
Bernhard Sick
315
16
0
05 Apr 2023
Multiview Representation Learning from Crowdsourced Triplet Comparisons
The Web Conference (WWW), 2023
Xiaotian Lu
Jiyi Li
Koh Takeuchi
H. Kashima
SSL
208
2
0
08 Feb 2023
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection
International Conference on Web and Social Media (ICWSM), 2022
Wenjie Yin
Vibhor Agarwal
Aiqi Jiang
A. Zubiaga
Nishanth R. Sastry
320
20
0
20 Dec 2022
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin
Tanguy Lefort
Benjamin Charlier
Alexis Joly
Joseph Salmon
411
6
0
30 Sep 2022
Meta Policy Learning for Cold-Start Conversational Recommendation
Web Search and Data Mining (WSDM), 2022
Zhendong Chu
Hongning Wang
Yun Xiao
Bo Long
Lingfei Wu
OffRL
324
44
0
24 May 2022
Trustable Co-label Learning from Multiple Noisy Annotators
IEEE transactions on multimedia (IEEE TMM), 2022
Shikun Li
Tongliang Liu
Jiyong Tan
Dan Zeng
Shiming Ge
NoLa
197
37
0
08 Mar 2022
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
International Conference on Natural Language and Speech Processing (ICNLSP), 2021
Gerhard Johann Hagerer
Dávid Szabó
Andreas Koch
Maria Luisa Ripoll Dominguez
Christian Widmer
Maximilian Wich
Hannah Danner
Georg Groh
272
8
0
03 Nov 2021
Learning from Crowds with Crowd-Kit
Dmitry Ustalov
Nikita Pavlichenko
B. Tseitlin
239
24
0
17 Sep 2021
Improve Learning from Crowds via Generative Augmentation
Knowledge Discovery and Data Mining (KDD), 2021
Zhendong Chu
Hongning Wang
222
15
0
22 Jul 2021
Learning from Crowds with Sparse and Imbalanced Annotations
Ye Shi
Shao-Yuan Li
Sheng-Jun Huang
277
6
0
11 Jul 2021
Learning from Multiple Annotators by Incorporating Instance Features
Jingzheng Li
Hailong Sun
Jiyi Li
Zhijun Chen
Renshuai Tao
Yufei Ge
NoLa
196
6
0
29 Jun 2021
Hypothesis Testing for Class-Conditional Label Noise
Rafael Poyiadzi
Weisong Yang
Niall Twomey
Raúl Santos-Rodríguez
NoLa
285
0
0
03 Mar 2021
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
ACM Transactions on Knowledge Discovery from Data (TKDD), 2020
Hanlu Wu
Tengfei Ma
Lingfei Wu
S. Ji
FedML
321
11
0
25 Oct 2020
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