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1808.05902
Cited By
Learning Supervised Topic Models for Classification and Regression from Crowds
17 August 2018
Filipe Rodrigues
Mariana Lourenço
B. Ribeiro
Francisco Câmara Pereira
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Papers citing
"Learning Supervised Topic Models for Classification and Regression from Crowds"
21 / 21 papers shown
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels
M. Herde
Lukas Lührs
Denis Huseljic
Bernhard Sick
432
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
400
6
0
26 Nov 2024
Learning to Complement and to Defer to Multiple Users
Zheng Zhang
Wenjie Ai
Kevin Wells
David Rosewarne
Thanh-Toan Do
Gustavo Carneiro
275
8
0
09 Jul 2024
A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement
Annerose Eichel
Sabine Schulte im Walde
277
4
0
05 Apr 2024
Learning to Complement with Multiple Humans
Pattern Recognition (Pattern Recogn.), 2023
Zheng Zhang
Cuong C. Nguyen
Kevin Wells
Thanh-Toan Do
Gustavo Carneiro
379
4
0
22 Nov 2023
Label Selection Approach to Learning from Crowds
International Conference on Neural Information Processing (ICONIP), 2023
Kosuke Yoshimura
H. Kashima
NoLa
126
1
0
21 Aug 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
249
26
0
05 Jun 2023
Expertise-based Weighting for Regression Models with Noisy Labels
Milene Regina dos Santos
Rafael Izbicki
NoLa
150
0
0
12 May 2023
Multi-annotator Deep Learning: A Probabilistic Framework for Classification
M. Herde
Denis Huseljic
Bernhard Sick
305
16
0
05 Apr 2023
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei
Zhaowei Zhu
Tianyi Luo
Ehsan Amid
Abhishek Kumar
Yang Liu
NoLa
250
46
0
14 Jun 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
Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals
Soft Computing - A Fusion of Foundations, Methodologies and Applications (Soft Computing), 2021
Bing Wang
Yue Wang
Ximing Li
Jihong Ouyang
183
4
0
20 Nov 2021
Learning from Multiple Annotators by Incorporating Instance Features
Jingzheng Li
Hailong Sun
Jiyi Li
Zhijun Chen
Renshuai Tao
Yufei Ge
NoLa
189
6
0
29 Jun 2021
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning
Findings (Findings), 2021
Xiang Zhou
Yixin Nie
Joey Tianyi Zhou
UQCV
316
33
0
18 Apr 2021
Pitfalls in Machine Learning Research: Reexamining the Development Cycle
Stella Biderman
Walter J. Scheirer
239
26
0
04 Nov 2020
Representation Learning from Limited Educational Data with Crowdsourced Labels
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020
Wentao Wang
Guowei Xu
Wenbiao Ding
Gale Yan Huang
Guoliang Li
Shucheng Zhou
Zitao Liu
SSL
262
16
0
23 Sep 2020
A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
D. Prijatelj
Mel McCurrie
Walter J. Scheirer
UQCV
201
2
0
20 Jun 2020
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Pablo Morales-Álvarez
Pablo Ruiz
S. Coughlin
Rafael Molina
Aggelos K. Katsaggelos
266
16
0
05 Nov 2019
Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study
IEEE International Conference on Data Engineering (ICDE), 2019
Guowei Xu
Wenbiao Ding
Shucheng Zhou
Songfan Yang
Gale Yan Huang
Zitao Liu
SSL
166
12
0
18 Jul 2019
Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification
Vaibhav Sinha
Sukrut Rao
V. Balasubramanian
274
34
0
07 Mar 2018
Deep learning from crowds
Filipe Rodrigues
Francisco Câmara Pereira
FedML
NoLa
270
298
0
06 Sep 2017
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