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Learning from Imperfect Annotations

Learning from Imperfect Annotations

7 April 2020
Emmanouil Antonios Platanios
Maruan Al-Shedivat
Eric Xing
Tom Michael Mitchell
ArXiv (abs)PDFHTML

Papers citing "Learning from Imperfect Annotations"

7 / 7 papers shown
Some Robustness Properties of Label Cleaning
Some Robustness Properties of Label Cleaning
Chen Cheng
John C. Duchi
203
0
0
14 Sep 2025
A Rate-Distortion View of Uncertainty Quantification
A Rate-Distortion View of Uncertainty Quantification
Ifigeneia Apostolopoulou
Benjamin Eysenbach
Frank Nielsen
Artur Dubrawski
UQCV
450
3
0
16 Jun 2024
Multi-annotator Deep Learning: A Probabilistic Framework for
  Classification
Multi-annotator Deep Learning: A Probabilistic Framework for Classification
M. Herde
Denis Huseljic
Bernhard Sick
307
16
0
05 Apr 2023
CROWDLAB: Supervised learning to infer consensus labels and quality
  scores for data with multiple annotators
CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators
Hui Wen Goh
Ulyana Tkachenko
Jonas W. Mueller
260
19
0
13 Oct 2022
Data Consistency for Weakly Supervised Learning
Data Consistency for Weakly Supervised Learning
Chidubem Arachie
Bert Huang
NoLa
210
2
0
08 Feb 2022
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label
  Sentiment Analysis
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment AnalysisInternational 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
264
8
0
03 Nov 2021
Constrained Labeling for Weakly Supervised Learning
Constrained Labeling for Weakly Supervised LearningConference on Uncertainty in Artificial Intelligence (UAI), 2020
Chidubem Arachie
Bert Huang
404
17
0
15 Sep 2020
1
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